802 lines
44 KiB
BibTeX
802 lines
44 KiB
BibTeX
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@article{carlucci_congestion_2017,
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title = {Congestion {Control} for {Web} {Real}-{Time} {Communication}},
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volume = {25},
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copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
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issn = {1063-6692, 1558-2566},
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url = {http://ieeexplore.ieee.org/document/7938641/},
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doi = {10.1109/TNET.2017.2703615},
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abstract = {Applications requiring real-time communication (RTC) between Internet peers are ever increasing. RTC requires not only congestion control but also minimization of queuing delays to provide interactivity. It is known that the wellestablished transmission control protocol congestion control is not suitable for RTC due to its retransmissions and in-order delivery mechanisms, which induce significant latency. In this paper, we propose a novel congestion control algorithm for RTC, which is based on the main idea of estimating—using a Kalman Filter—the end-to-end one-way delay variation which is experienced by packets traveling from a sender to a destination. This estimate is compared with a dynamic threshold and drives the dynamics of a controller located at the receiver, which aims at maintaining queuing delays low, while a loss-based controller located at the sender acts when losses are detected. The proposed congestion control algorithm has been adopted by Google Chrome. Extensive experimental evaluations have shown that the algorithm contains queuing delays while providing intra and inter protocol fairness along with full link utilization.},
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language = {en},
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number = {5},
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urldate = {2026-06-11},
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journal = {IEEE/ACM Transactions on Networking},
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author = {Carlucci, Gaetano and De Cicco, Luca and Holmer, Stefan and Mascolo, Saverio},
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month = oct,
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year = {2017},
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pages = {2629--2642},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\SBVBMBYH\\Carlucci 等 - 2017 - Congestion Control for Web Real-Time Communication.pdf:application/pdf},
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}
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@inproceedings{carlucci_making_2016,
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address = {Berlin Germany},
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title = {Making {Google} {Congestion} {Control} robust over {Wi}-{Fi} networks using packet grouping},
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isbn = {978-1-4503-4443-2},
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url = {https://dl.acm.org/doi/10.1145/2959424.2959431},
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doi = {10.1145/2959424.2959431},
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abstract = {Google congestion control (GCC) has been proposed for the case of delay sensitive traffic (i.e. video-conference) in the WebRTC framework. In this paper we analyze the effect of wireless channel outages on the GCC. We have observed that, when a channel outage ends, there are packets that arrive at the receiver as a burst. This behavior impairs the delay-based controller employed by GCC, resulting in throughput degradation. We propose a solution to make GCC robust with respect to channel outages. In particular, by grouping packets that arrive in a burst, the delay-based controller avoids to misinterpret a burst as network congestion. In order to prove the effectiveness of the proposed solution we have carried out a trace-driven experimental evaluation in a loaded Wi-Fi scenario.},
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language = {en},
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urldate = {2026-06-11},
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booktitle = {Proceedings of the 2016 {Applied} {Networking} {Research} {Workshop}},
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publisher = {ACM},
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author = {Carlucci, Gaetano and De Cicco, Luca and Holmer, Stefan and Mascolo, Saverio},
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month = jul,
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year = {2016},
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pages = {74--80},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\NL9HPGCL\\Carlucci 等 - 2016 - Making Google Congestion Control robust over Wi-Fi networks using packet grouping.pdf:application/pdf},
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}
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@inproceedings{dai_learning-based_2018,
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address = {Florence},
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title = {Learning-based {Congestion} {Control} for {Internet} {Video} {Communication} over {Wireless} {Networks}},
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isbn = {978-1-5386-4881-0},
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url = {https://ieeexplore.ieee.org/document/8351530/},
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doi = {10.1109/ISCAS.2018.8351530},
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abstract = {With the deployment of real-time video applications and wireless networks, the real-time congestion control becomes a hot topic. Most existing congestion control algorithms are not designed for low-latency real-time flows, or perform poorly in the face of highly variable channel capacities. In this paper, we proposed a novel Learning-based Congestion Control (LCC) for real-time video communication over wireless networks. The key idea of LCC is employing Kernel Density Estimation for oneway delay and sending rate to capture the underlying information about channel state. Then LCC bases on the estimated probability density and Bayesian theorem to quickly adapt sending rate to the changing channel. We implemented LCC in WebRTC framework and extensive experiments were carried out. Compared with the native WebRTC congestion control (GCC), experimental results show that LCC achieves higher channel utilization, even more than 4.2ˆ throughput in lossy links. LCC is also much better at adapting to the variable channel than GCC. Besides, LCC performs well in delay constraint and intra-protocol fairness.},
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language = {en},
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urldate = {2026-06-11},
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booktitle = {2018 {IEEE} {International} {Symposium} on {Circuits} and {Systems} ({ISCAS})},
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publisher = {IEEE},
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author = {Dai, Tongyu and Zhang, Xinggong and Guo, Zongming},
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year = {2018},
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pages = {1--5},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\85BU4EP7\\Dai 等 - 2018 - Learning-based Congestion Control for Internet Video Communication over Wireless Networks.pdf:application/pdf},
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}
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@article{dai_statistical_2020,
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title = {Statistical {Learning} {Based} {Congestion} {Control} for {Real}-{Time} {Video} {Communication}},
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volume = {22},
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copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
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issn = {1520-9210, 1941-0077},
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url = {https://ieeexplore.ieee.org/document/8931660/},
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doi = {10.1109/TMM.2019.2959448},
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abstract = {The existing congestion control is hard to simultaneously achieve low latency, high throughput, good adaptability and fair bandwidth allocation, mainly because of the hardwired control strategy and egocentric convergence objective. To address these issues, we propose an end-to-end statistical learning based congestion control, named Iris. By exploring the underlying principles of self-inflicted delay, we find that RTT variation is linearly related to the difference between sending rate and receiving rate, which inspires us to control video bit rate using a statistical-learning congestion control model. The key idea of Iris is to force all flows to converge to the same queue load and adjust bit rate by the model. All flows keep a small and fixed number of packets queuing in the network, thus the fair bandwidth allocation and low latency are both achieved. Besides, the adjustment step size of sending rate is updated by online learning, to better adapt to dynamically changing networks. We carried out extensive experiments to evaluate the performance of Iris, with the implementations over transport layer and application layer respectively. The testing environment includes emulated network, real-world Internet and commercial cellular networks. Compared against Transmission Control Protocol (TCP) flavors and state-of-the-art protocols, Iris is able to achieve high bandwidth utilization, low latency and good fairness concurrently. Especially for HyperText Transfer Protocol (HTTP) video streaming service, Iris is able to increase the video bitrate up to 25\% and Peak Signal to Noise Ratio (PSNR) up to 1 dB.},
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language = {en},
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number = {10},
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urldate = {2026-06-11},
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journal = {IEEE Transactions on Multimedia},
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author = {Dai, Tongyu and Zhang, Xinggong and Zhang, Yihang and Guo, Zongming},
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month = oct,
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year = {2020},
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pages = {2672--2683},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\PIV8XYSW\\Dai 等 - 2020 - Statistical Learning Based Congestion Control for Real-Time Video Communication.pdf:application/pdf},
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}
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@misc{karimi_vidaptive_2024,
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title = {Vidaptive: {Efficient} and {Responsive} {Rate} {Control} for {Real}-{Time} {Video} on {Variable} {Networks}},
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shorttitle = {Vidaptive},
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url = {http://arxiv.org/abs/2309.16869},
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doi = {10.48550/arXiv.2309.16869},
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abstract = {Real-time video streaming relies on rate control mechanisms to adapt video bitrate to network capacity while maintaining high utilization and low delay. However, the current video rate controllers, such as Google Congestion Control (GCC), are very slow to respond to network changes, leading to link under-utilization and latency spikes. While recent delaybased congestion control algorithms promise high efficiency and rapid adaptation to variable conditions, low-latency video applications have been unable to adopt these schemes due to the intertwined relationship between video encoders and rate control in current systems.},
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language = {en},
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urldate = {2026-06-11},
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publisher = {arXiv},
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author = {Karimi, Pantea and Fouladi, Sadjad and Sivaraman, Vibhaalakshmi and Alizadeh, Mohammad},
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month = feb,
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year = {2024},
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note = {arXiv:2309.16869 [cs.NI]},
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keywords = {Computer Science - Networking and Internet Architecture},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\YX7FGX8G\\Karimi 等 - 2024 - Vidaptive Efficient and Responsive Rate Control for Real-Time Video on Variable Networks.pdf:application/pdf},
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}
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@inproceedings{xie_pbe-cc_2020,
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address = {Virtual Event USA},
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title = {{PBE}-{CC}: {Congestion} {Control} via {Endpoint}-{Centric}, {Physical}-{Layer} {Bandwidth} {Measurements}},
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isbn = {978-1-4503-7955-7},
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shorttitle = {{PBE}-{CC}},
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url = {https://dl.acm.org/doi/10.1145/3387514.3405880},
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doi = {10.1145/3387514.3405880},
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abstract = {Cellular networks are becoming ever more sophisticated and overcrowded, imposing the most delay, jitter, and throughput damage to end-to-end network flows in today’s internet. We therefore argue for fine-grained mobile endpoint-based wireless measurements to inform a precise congestion control algorithm through a welldefined API to the mobile’s cellular physical layer. Our proposed congestion control algorithm is based on Physical-Layer Bandwidth measurements taken at the Endpoint (PBE-CC), and captures the latest 5G New Radio innovations that increase wireless capacity, yet create abrupt rises and falls in available wireless capacity that the PBE-CC sender can react to precisely and rapidly. We implement a proof-of-concept prototype of the PBE measurement module on software-defined radios and the PBE sender and receiver in C. An extensive performance evaluation compares PBE-CC head to head against the cellular-aware and wireless-oblivious congestion control protocols proposed in the research community and in deployment, in mobile and static mobile scenarios, and over busy and idle networks. Results show 6.3\% higher average throughput than BBR, while simultaneously reducing 95th percentile delay by 1.8×.},
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language = {en},
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urldate = {2026-06-11},
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booktitle = {Proceedings of the {Annual} conference of the {ACM} {Special} {Interest} {Group} on {Data} {Communication} on the applications, technologies, architectures, and protocols for computer communication},
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publisher = {ACM},
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author = {Xie, Yaxiong and Yi, Fan and Jamieson, Kyle},
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month = jul,
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year = {2020},
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pages = {451--464},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\KICDNXP9\\Xie 等 - 2020 - PBE-CC Congestion Control via Endpoint-Centric, Physical-Layer Bandwidth Measurements.pdf:application/pdf},
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}
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@misc{goyal_abc_2021,
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title = {{ABC}: {A} {Simple} {Explicit} {Congestion} {Controller} for {Wireless} {Networks}},
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shorttitle = {{ABC}},
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url = {http://arxiv.org/abs/1905.03429},
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doi = {10.48550/arXiv.1905.03429},
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abstract = {We propose Accel-Brake Control (ABC), a simple and deployable explicit congestion control protocol for network paths with time-varying wireless links. ABC routers mark each packet with an “accelerate” or “brake”, which causes senders to slightly increase or decrease their congestion windows. Routers use this feedback to quickly guide senders towards a desired target rate. ABC requires no changes to header formats or user devices, but achieves better performance than XCP. ABC is also incrementally deployable; it operates correctly when the bottleneck is a non-ABC router, and can coexist with non-ABC traffic sharing the same bottleneck link. We evaluate ABC using a Wi-Fi implementation and trace-driven emulation of cellular links. ABC achieves 30-40\% higher throughput than Cubic+Codel for similar delays, and 2.2× lower delays than BBR on a Wi-Fi path. On cellular network paths, ABC achieves 50\% higher throughput than Cubic+Codel.},
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language = {en},
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urldate = {2026-06-11},
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publisher = {arXiv},
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author = {Goyal, Prateesh and Agarwal, Anup and Netravali, Ravi and Alizadeh, Mohammad and Balakrishnan, Hari},
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month = mar,
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year = {2021},
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note = {arXiv:1905.03429 [cs.NI]},
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keywords = {Computer Science - Networking and Internet Architecture},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\ZHEPVNJA\\Goyal 等 - 2021 - ABC A Simple Explicit Congestion Controller for Wireless Networks.pdf:application/pdf},
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}
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@inproceedings{chen_videollm-online_2024,
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address = {Seattle, WA, USA},
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title = {{VideoLLM}-online: {Online} {Video} {Large} {Language} {Model} for {Streaming} {Video}},
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copyright = {https://doi.org/10.15223/policy-029},
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isbn = {979-8-3503-5300-6},
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shorttitle = {{VideoLLM}-online},
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url = {https://ieeexplore.ieee.org/document/10657274/},
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doi = {10.1109/CVPR52733.2024.01742},
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language = {en},
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urldate = {2026-06-11},
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booktitle = {2024 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
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publisher = {IEEE},
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author = {Chen, Joya and Lv, Zhaoyang and Wu, Shiwei and Lin, Kevin Qinghong and Song, Chenan and Gao, Difei and Liu, Jia-Wei and Gao, Ziteng and Mao, Dongxing and Shou, Mike Zheng},
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month = jun,
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year = {2024},
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pages = {18407--18418},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\HYUNXZ24\\Chen 等 - 2024 - VideoLLM-online Online Video Large Language Model for Streaming Video.pdf:application/pdf},
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}
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@inproceedings{niu_ovo-bench_2025,
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address = {Nashville, TN, USA},
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title = {{OVO}-{Bench}: {How} {Far} is {Your} {Video}-{LLMs} from {Real}-{World} {Online} {Video} {Understanding}?},
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copyright = {https://doi.org/10.15223/policy-029},
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isbn = {979-8-3315-4364-8},
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shorttitle = {{OVO}-{Bench}},
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url = {https://ieeexplore.ieee.org/document/11094230/},
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doi = {10.1109/CVPR52734.2025.01761},
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language = {en},
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urldate = {2026-06-11},
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booktitle = {2025 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
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publisher = {IEEE},
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author = {Niu, Junbo and Li, Yifei and Miao, Ziyang and Ge, Chunjiang and Zhou, Yuanhang and He, Qihao and Dong, Xiaoyi and Duan, Haodong and Ding, Shuangrui and Qian, Rui and Zhang, Pan and Zang, Yuhang and Cao, Yuhang and He, Conghui and Wang, Jiaqi},
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month = jun,
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year = {2025},
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pages = {18902--18913},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\2AXRLIKN\\Niu 等 - 2025 - OVO-Bench How Far is Your Video-LLMs from Real-World Online Video Understanding.pdf:application/pdf},
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}
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@misc{zou_codecsight_2026,
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title = {{CodecSight}: {Leveraging} {Video} {Codec} {Signals} for {Efficient} {Streaming} {VLM} {Inference}},
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shorttitle = {{CodecSight}},
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url = {http://arxiv.org/abs/2604.06036},
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doi = {10.48550/arXiv.2604.06036},
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abstract = {Video streaming analytics is a crucial workload for visionlanguage model serving, but the high cost of multimodal inference limits scalability. Prior systems reduce inference cost by exploiting temporal and spatial redundancy in video streams, but they target either the vision transformer (ViT) or the LLM with a limited view, leaving end-to-end opportunities untapped. Moreover, existing methods incur significant overhead to identify redundancy, either through offline profiling and training or costly online computation, making them ill-suited for dynamic real-time streams.},
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language = {en},
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urldate = {2026-06-11},
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publisher = {arXiv},
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author = {Zou, Yulin and Chen, Yan and Chen, Wenyan and Park, JooYoung and Nitin, Shivaraman and Tao, Luo and Romero, Francisco and Ustiugov, Dmitrii},
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month = apr,
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year = {2026},
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note = {arXiv:2604.06036 [cs.DC]},
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keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning},
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annote = {Comment: 18 pages, 34 figures},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\FKDV6KU9\\Zou 等 - 2026 - CodecSight Leveraging Video Codec Signals for Efficient Streaming VLM Inference.pdf:application/pdf},
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}
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@misc{ahmad_vision-language_2026,
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title = {Vision-{Language} {Models} on the {Edge} for {Real}-{Time} {Robotic} {Perception}},
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url = {http://arxiv.org/abs/2601.14921},
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doi = {10.48550/arXiv.2601.14921},
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abstract = {Vision–Language Models (VLMs) enable multimodal reasoning for robotic perception and interaction, but their deployment in real-world systems remains constrained by latency, limited onboard resources, and privacy risks of cloud offloading. Edge intelligence within 6G, particularly Open RAN and Multi-access Edge Computing (MEC), offers a pathway to address these challenges by bringing computation closer to the data source. This work investigates the deployment of VLMs on ORAN/MEC infrastructure using the Unitree G1 humanoid robot as an embodied testbed. We design a WebRTC-based pipeline that streams multimodal data to an edge node and evaluate LLaMA-3.2-11B-Vision-Instruct deployed at the edge versus in the cloud under real-time conditions. Our results show that edge deployment preserves near-cloud accuracy while reducing end-to-end latency by 5\%. We further evaluate Qwen2-VL-2BInstruct, a compact model optimized for resource-constrained environments, which achieves sub-second responsiveness, cutting latency by more than half but at the cost of accuracy.},
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language = {en},
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urldate = {2026-06-11},
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publisher = {arXiv},
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author = {Ahmad, Sarat and Hafeez, Maryam and Zaidi, Syed Ali Raza},
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month = jan,
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year = {2026},
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note = {arXiv:2601.14921 [cs.RO]},
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keywords = {Computer Science - Artificial Intelligence, Computer Science - Robotics},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\VKP58GQE\\Ahmad 等 - 2026 - Vision-Language Models on the Edge for Real-Time Robotic Perception.pdf:application/pdf},
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}
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@article{casasnovas_experimental_2024,
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title = {Experimental evaluation of interactive {Edge}/{Cloud} {Virtual} {Reality} gaming over {Wi}-{Fi} using unity render streaming},
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volume = {226-227},
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issn = {01403664},
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url = {https://linkinghub.elsevier.com/retrieve/pii/S0140366424002585},
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doi = {10.1016/j.comcom.2024.08.001},
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abstract = {Virtual Reality (VR) streaming enables end-users to seamlessly immerse themselves in interactive virtual environments using even low-end devices. However, the quality of the VR experience heavily relies on Wireless Fidelity (Wi-Fi) performance, since it serves as the last hop in the network chain. Our study delves into the intricate interplay between Wi-Fi and VR traffic, drawing upon empirical data and leveraging a Wi-Fi simulator. In this work, we further evaluate Wi-Fi’s suitability for VR streaming in terms of the Quality of Service (QoS) it provides. In particular, we employ Unity Render Streaming to remotely stream real-time VR gaming content over Wi-Fi 6 using Web Real-Time Communication (WebRTC), considering a server physically located at the network’s edge, near the end user. Our findings demonstrate the system’s sustained network performance, showcasing minimal round-trip time (RTT) and jitter at 60 and 90 frames per second (fps). In addition, we uncover the characteristics and patterns of the generated traffic streams, unveiling a distinctive video transmission approach inherent to WebRTC-based services: the systematic packetization of video frames (VFs) and their transmission in discrete batches at regular intervals, regardless of the targeted frame rate. This interval-based transmission strategy maintains consistent video packet delays across video frame rates but leads to increased Wi-Fi airtime consumption. Our results demonstrate that shortening the interval between batches is advantageous, as it enhances Wi-Fi efficiency and reduces delays in delivering complete frames.},
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language = {en},
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urldate = {2026-06-11},
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journal = {Computer Communications},
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author = {Casasnovas, Miguel and Michaelides, Costas and Carrascosa-Zamacois, Marc and Bellalta, Boris},
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month = oct,
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year = {2024},
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pages = {107919},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\ISGDXY58\\Casasnovas 等 - 2024 - Experimental evaluation of interactive EdgeCloud Virtual Reality gaming over Wi-Fi using unity rend.pdf:application/pdf},
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}
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@misc{guo_blade_2026,
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title = {{BLADE}: {Adaptive} {Wi}-{Fi} {Contention} {Control} for {Next}-{Generation} {Real}-{Time} {Communication}},
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copyright = {arXiv.org perpetual, non-exclusive license},
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shorttitle = {{BLADE}},
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url = {https://arxiv.org/abs/2603.16119},
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doi = {10.48550/ARXIV.2603.16119},
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abstract = {Next-generation real-time communication (NGRTC) applications, such as cloud gaming and XR, demand consistently ultra-low latency. However, through our first large-scale measurement, we find that despite the deployment of edge servers, dedicated congestion control, and loss recovery mechanisms, cloud gaming users still experience long-tail latency in WiFi networks. We further identify that Wi-Fi last-mile access points (APs) serve as the primary latency bottleneck. Specifically, short-term packet delivery droughts, caused by fundamental limitations in Wi-Fi contention control standards, are the root cause. To address this issue, we propose BLADE, an adaptive contention control algorithm that dynamically adjusts the contention windows (CW) of all Wi-Fi transmitters based on the channel contention level in a fully distributed manner. Our ns3 simulations and real-world evaluations with commercial Wi-Fi APs demonstrate that, compared to standard contention control, BLADE reduces Wi-Fi packet transmission tail latency by over 5× under heavy channel contention and significantly stabilizes MAC throughput while ensuring fast and fair convergence. Consequently, BLADE reduces the video stall rate in cloud gaming by over 90\%.},
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language = {en},
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urldate = {2026-06-11},
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publisher = {arXiv},
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author = {Guo, Fengqian and Zhou, Yuhan and Jiang, Longwei and Miao, Congcong and Liu, Yuxin and Xu, Chenren and Lu, Hancheng and Chen, Chang Wen and Xie, Yaxiong and Liu, Honghao},
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year = {2026},
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note = {Version Number: 1},
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keywords = {FOS: Computer and information sciences, Networking and Internet Architecture (cs.NI)},
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file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\CAY73MEU\\Guo 等 - 2026 - BLADE Adaptive Wi-Fi Contention Control for Next-Generation Real-Time Communication.pdf:application/pdf},
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}
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@inproceedings{kulkarni_understanding_2023,
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address = {New York NY USA},
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title = {Understanding the {Impact} of {Wi}-{Fi} {Configuration} on {Volumetric} {Video} {Streaming} {Applications}},
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isbn = {979-8-4007-0303-4},
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url = {https://dl.acm.org/doi/10.1145/3609395.3610599},
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doi = {10.1145/3609395.3610599},
|
||
abstract = {Emerging multimedia applications often use a wireless LAN (Wi-Fi) infrastructure to stream content. These Wi-Fi deployments vary vastly in terms of their system configurations. In this paper, we take a step toward characterizing the Quality of Experience (QoE) of volumetric video streaming over an enterprise-grade Wi-Fi network to: (i) understand the impact of Wi-Fi control parameters on user QoE, (ii) analyze the relation between Quality of Service (QoS) metrics of Wi-Fi networks and application QoE, and (iii) compare the QoE of volumetric video streaming to traditional 2D video applications. We find that Wi-Fi configuration parameters such as channel width, radio interface, access category, and priority queues are important for optimizing Wi-Fi networks for streaming immersive videos.},
|
||
language = {en},
|
||
urldate = {2026-06-11},
|
||
booktitle = {Proceedings of the 2023 {Workshop} on {Emerging} {Multimedia} {Systems}},
|
||
publisher = {ACM},
|
||
author = {Kulkarni, Umakant and Diab, Khaled and Aggarwal, Shivang and Cao, Lianjie and Ahmed, Faraz and Sharma, Puneet and Fahmy, Sonia},
|
||
month = sep,
|
||
year = {2023},
|
||
pages = {41--47},
|
||
file = {PDF:C\:\\Users\\26815\\Zotero\\storage\\FGLYZTY2\\Kulkarni 等 - 2023 - Understanding the Impact of Wi-Fi Configuration on Volumetric Video Streaming Applications.pdf:application/pdf},
|
||
}
|
||
|
||
@inproceedings{ns3_wimac,
|
||
author = {Mi, Chengxiang and Gu, Jingyi and Zhang, Chengwei and Gao, Yayu},
|
||
title = {ns3-WiMacStat: Enabling In-Depth WiFi MAC Performance Analysis via RadioTap-Based Statistics},
|
||
year = {2025},
|
||
isbn = {9798400715174},
|
||
publisher = {Association for Computing Machinery},
|
||
address = {New York, NY, USA},
|
||
url = {https://doi.org/10.1145/3747204.3747215},
|
||
doi = {10.1145/3747204.3747215},
|
||
booktitle = {Proceedings of the 2025 International Conference on Ns-3},
|
||
pages = {29–37},
|
||
numpages = {9},
|
||
keywords = {ns-3, Statistical Analysis, Visualization},
|
||
series = {ICNS3 '25}
|
||
}
|
||
|
||
@article{JDCP202406008,
|
||
author = {陈震 and 卢金勤 and 朱奇 and 朱磊},
|
||
title = {基于WebRTC的低延迟流媒体传输方法研究},
|
||
journal = {机电产品开发与创新},
|
||
volume = {37},
|
||
number = {06},
|
||
pages = {29-31},
|
||
year = {2024},
|
||
issn = {1002-6673},
|
||
}
|
||
@article{JSJA202410013,
|
||
author = {牛冠冲 and 刘飞翔 and 杨雯 and 苗启广 and 毛亮},
|
||
title = {基于边缘计算和WebRTC的元宇宙教育通信技术方案研究与实现},
|
||
journal = {计算机科学},
|
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volume = {51},
|
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number = {10},
|
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pages = {94-104},
|
||
year = {2024},
|
||
issn = {1002-137X},
|
||
}
|
||
@article{XXWX202308018,
|
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author = {高朝龙 and 张景波 and 蔡星娟},
|
||
title = {WebRTC视频会议的高维多目标多码率优化研究},
|
||
journal = {小型微型计算机系统},
|
||
volume = {44},
|
||
number = {08},
|
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pages = {1735-1742},
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year = {2023},
|
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issn = {1000-1220},
|
||
doi = {10.20009/j.cnki.21-1106/TP.2021-0899}
|
||
}
|
||
@mastersthesis{1016074228.nh,
|
||
author = {闫素英},
|
||
title = {基于WebRTC的无线实时通信QoS-QoE评估与预测},
|
||
school = {北京交通大学},
|
||
year = {2016}
|
||
}
|
||
|
||
@article{ DZQJ202504022,
|
||
author = { 徐嵩 and 王尔申 and 郭倩文 and 舒皖森 and 娜拉 },
|
||
title = {基于5G及WebRTC的车辆行驶状态实时监控系统设计},
|
||
journal = {电子器件},
|
||
volume = {48},
|
||
number = {04},
|
||
pages = {863-868},
|
||
year = {2025},
|
||
issn = {1005-9490},
|
||
}
|
||
|
||
@inproceedings{LiYueheng,
|
||
author = {Li, Yueheng and Zhang, Zicheng and Chen, Hao and Ma, Zhan},
|
||
title = {Mamba: Bringing Multi-Dimensional ABR to WebRTC},
|
||
year = {2023},
|
||
isbn = {9798400701085},
|
||
publisher = {Association for Computing Machinery},
|
||
address = {New York, NY, USA},
|
||
url = {https://doi.org/10.1145/3581783.3611915},
|
||
doi = {10.1145/3581783.3611915},
|
||
booktitle = {Proceedings of the 31st ACM International Conference on Multimedia},
|
||
pages = {9262–9270},
|
||
numpages = {9},
|
||
keywords = {adaptive bitrate (abr), multi-agent reinforcement learning, multi-dimensional abr, qoe},
|
||
location = {Ottawa ON, Canada},
|
||
series = {MM '23}
|
||
}
|
||
|
||
@article{Smirnov,
|
||
author = {Smirnov, Nikita and Tomforde, Sven},
|
||
title = {Real-time rate control of WebRTC video streams in 5G networks: Improving quality of experience with Deep Reinforcement Learning},
|
||
year = {2024},
|
||
issue_date = {Mar 2024},
|
||
publisher = {Elsevier North-Holland, Inc.},
|
||
address = {USA},
|
||
volume = {148},
|
||
number = {C},
|
||
issn = {1383-7621},
|
||
url = {https://doi.org/10.1016/j.sysarc.2024.103066},
|
||
doi = {10.1016/j.sysarc.2024.103066},
|
||
journal = {J. Syst. Archit.},
|
||
month = mar,
|
||
numpages = {9},
|
||
keywords = {Deep reinforcement learning, Real-time communication, 5G networks, Webrtc, Adaptive bitrate streaming, Congestion control, Remote-controlled device, Organic computing}
|
||
}
|
||
|
||
@article{Encheng,
|
||
author = {Yu, Encheng and Zhou, Jianer and Li, Zhenyu and Tyson, Gareth and Li, Weichao and Zhang, Xinyi and Xu, Zhiwei and Xie, Gaogang},
|
||
title = {Mustang: Improving QoE for Real-Time Video in Cellular Networks by Masking Jitter},
|
||
year = {2024},
|
||
issue_date = {September 2024},
|
||
publisher = {Association for Computing Machinery},
|
||
address = {New York, NY, USA},
|
||
volume = {20},
|
||
number = {9},
|
||
issn = {1551-6857},
|
||
url = {https://doi.org/10.1145/3672399},
|
||
doi = {10.1145/3672399},
|
||
journal = {ACM Trans. Multimedia Comput. Commun. Appl.},
|
||
month = sep,
|
||
articleno = {285},
|
||
numpages = {23},
|
||
keywords = {WebRTC, GCC, real-time video, cellular network}
|
||
}
|
||
|
||
@inproceedings{RFEC,
|
||
author = {Lee, Insoo and Kim, Seyeon and Sathyanarayana, Sandesh and Bin, Kyungmin and Chong, Song and Lee, Kyunghan and Grunwald, Dirk and Ha, Sangtae},
|
||
title = {R-FEC: RL-based FEC Adjustment for Better QoE in WebRTC},
|
||
year = {2022},
|
||
isbn = {9781450392037},
|
||
publisher = {Association for Computing Machinery},
|
||
address = {New York, NY, USA},
|
||
url = {https://doi.org/10.1145/3503161.3548370},
|
||
doi = {10.1145/3503161.3548370},
|
||
booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
|
||
pages = {2948–2956},
|
||
numpages = {9},
|
||
keywords = {qoe, rate control, video conferencing},
|
||
location = {Lisboa, Portugal},
|
||
series = {MM '22}
|
||
}
|
||
|
||
@article{Cheng2023,
|
||
author = {Cheng, Sheng and Hu, Han and Zhang, Xinggong},
|
||
title = {ABRF: Adaptive BitRate-FEC Joint Control for Real-Time Video Streaming},
|
||
year = {2023},
|
||
issue_date = {Sept. 2023},
|
||
publisher = {IEEE Press},
|
||
volume = {33},
|
||
number = {9},
|
||
issn = {1051-8215},
|
||
url = {https://doi.org/10.1109/TCSVT.2023.3248164},
|
||
doi = {10.1109/TCSVT.2023.3248164},
|
||
journal = {IEEE Trans. Cir. and Sys. for Video Technol.},
|
||
month = sep,
|
||
pages = {5212–5226},
|
||
numpages = {15}
|
||
}
|
||
|
||
@inproceedings {Shen2026,
|
||
author = {Yixin Shen and Ruihua Chen and Bo Wang and Jing Chen and Haochen Zhang and Minhu Wang and Mingwei Xu and Zili Meng},
|
||
title = {Mortise: Auto-tuning Congestion Control to Optimize {QoE} via {Network-Aware} Parameter Optimization},
|
||
booktitle = {23rd USENIX Symposium on Networked Systems Design and Implementation (NSDI 26)},
|
||
year = {2026},
|
||
isbn = {978-1-939133-54-0},
|
||
address = {Renton, WA},
|
||
pages = {2303--2321},
|
||
url = {https://www.usenix.org/conference/nsdi26/presentation/shen-yixin},
|
||
publisher = {USENIX Association},
|
||
month = {may}
|
||
}
|
||
|
||
@INPROCEEDINGS{bankov_tuning_2020,
|
||
author={Bankov, Dmitry and Chemrov, Kirill and Khorov, Evgeny},
|
||
booktitle={2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)},
|
||
title={Tuning Channel Access to Enable Real-Time Applications in Wi-Fi 7},
|
||
year={2020},
|
||
volume={},
|
||
number={},
|
||
pages={20-25},
|
||
doi={10.1109/ICUMT51630.2020.9222409}}
|
||
|
||
|
||
@article{adame_tsn_2021,
|
||
AUTHOR = {Adame, Toni and Carrascosa-Zamacois, Marc and Bellalta, Boris},
|
||
TITLE = {Time-Sensitive Networking in IEEE 802.11be: On the Way to Low-Latency WiFi 7},
|
||
JOURNAL = {Sensors},
|
||
VOLUME = {21},
|
||
YEAR = {2021},
|
||
NUMBER = {15},
|
||
ARTICLE-NUMBER = {4954},
|
||
URL = {https://www.mdpi.com/1424-8220/21/15/4954},
|
||
PubMedID = {34372190},
|
||
ISSN = {1424-8220},
|
||
DOI = {10.3390/s21154954}
|
||
}
|
||
|
||
@inproceedings{du_revisiting_2024,
|
||
author = {Du, Xinle and Li, Jie and Shao, Yiyang and Wang, Wei and Hu, Shuihai and Zhou, Jingbin and Tan, Kun},
|
||
title = {Revisiting Congestion Control for WiFi Networks},
|
||
year = {2024},
|
||
isbn = {9798400717581},
|
||
publisher = {Association for Computing Machinery},
|
||
address = {New York, NY, USA},
|
||
url = {https://doi.org/10.1145/3663408.3663421},
|
||
doi = {10.1145/3663408.3663421},
|
||
booktitle = {Proceedings of the 8th Asia-Pacific Workshop on Networking},
|
||
pages = {88–94},
|
||
numpages = {7},
|
||
keywords = {Congestion Control, WiFi Networks},
|
||
location = {Sydney, Australia},
|
||
series = {APNet '24}
|
||
}
|
||
|
||
|
||
@inproceedings{yi_athena_2024,
|
||
author = {Yi, Fan and Wan, Haoran and Jamieson, Kyle and Rexford, Jennifer and Xie, Yaxiong and Michel, Oliver},
|
||
title = {Athena: Seeing and Mitigating Wireless Impact on Video Conferencing and Beyond},
|
||
year = {2024},
|
||
isbn = {9798400712722},
|
||
publisher = {Association for Computing Machinery},
|
||
address = {New York, NY, USA},
|
||
url = {https://doi.org/10.1145/3696348.3696889},
|
||
doi = {10.1145/3696348.3696889},
|
||
booktitle = {Proceedings of the 23rd ACM Workshop on Hot Topics in Networks},
|
||
pages = {103–110},
|
||
numpages = {8},
|
||
keywords = {5G Networks, Network Measurement, Video Conferencing},
|
||
location = {Irvine, CA, USA},
|
||
series = {HotNets '24}
|
||
}
|
||
|
||
@inproceedings {shen_law_2026,
|
||
author = {Yibin Shen and Zili Meng},
|
||
title = {Law: Towards Consistent Low Latency in 802.11 Home Networks},
|
||
booktitle = {23rd USENIX Symposium on Networked Systems Design and Implementation (NSDI 26)},
|
||
year = {2026},
|
||
isbn = {978-1-939133-54-0},
|
||
address = {Renton, WA},
|
||
pages = {1741--1757},
|
||
url = {https://www.usenix.org/conference/nsdi26/presentation/shen-yibin},
|
||
publisher = {USENIX Association},
|
||
month = {may}
|
||
}
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||
|
||
@inproceedings {gong_flick_2025,
|
||
author = { Gong, Qianyun and Xu, Jiapei and Shi, Jianxin and Yuan, Xinjing and Pu, Lingjun and Xu, Jingdong },
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||
booktitle = { 2025 IEEE 33rd International Conference on Network Protocols (ICNP) },
|
||
title = {{ Flick: Frame-Perceptive Packet Scheduling for Low-Latency Video Services in Wi-Fi Networks }},
|
||
year = {2025},
|
||
volume = {},
|
||
ISSN = {},
|
||
pages = {1-13},
|
||
doi = {10.1109/ICNP65844.2025.11192371},
|
||
url = {https://doi.ieeecomputersociety.org/10.1109/ICNP65844.2025.11192371},
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||
publisher = {IEEE Computer Society},
|
||
address = {Los Alamitos, CA, USA},
|
||
month = {sep}}
|
||
|
||
@inproceedings{carrascosa_mlo_2022,
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||
author={Carrascosa, Marc and Geraci, Giovanni and Knightly, Edward and Bellalta, Boris},
|
||
booktitle={ICC 2022 - IEEE International Conference on Communications},
|
||
title={An Experimental Study of Latency for IEEE 802.11be Multi-link Operation},
|
||
year={2022},
|
||
volume={},
|
||
number={},
|
||
pages={2507-2512},
|
||
doi={10.1109/ICC45855.2022.9838765}}
|
||
|
||
@misc{ning_livevlm_2025,
|
||
title = {LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval},
|
||
author = {Ning, Zhenyu and Liu, Guangda and Jin, Qihao and Ding, Wenchao and Guo, Minyi and Zhao, Jieru},
|
||
year = {2025},
|
||
eprint = {2505.15269},
|
||
archivePrefix = {arXiv},
|
||
primaryClass = {cs.CV}
|
||
}
|
||
|
||
@misc{xu_streamingvlm_2025,
|
||
title={StreamingVLM: Real-Time Understanding for Infinite Video Streams},
|
||
author={Ruyi Xu and Guangxuan Xiao and Yukang Chen and Liuning He and Yao Lu and Song Han},
|
||
year={2026},
|
||
eprint={2510.09608},
|
||
archivePrefix={arXiv},
|
||
primaryClass={cs.CV},
|
||
url={https://arxiv.org/abs/2510.09608},
|
||
}
|
||
|
||
@misc{li_videoscan_2025,
|
||
title={VideoScan: Enabling Efficient Streaming Video Understanding via Frame-level Semantic Carriers},
|
||
author={Ruanjun Li and Yuedong Tan and Yuanming Shi and Jiawei Shao},
|
||
year={2025},
|
||
eprint={2503.09387},
|
||
archivePrefix={arXiv},
|
||
primaryClass={cs.CV},
|
||
url={https://arxiv.org/abs/2503.09387},
|
||
}
|
||
|
||
@misc{shaker_mobilevideogpt_2025,
|
||
title={Mobile-VideoGPT: Fast and Accurate Model for Mobile Video Understanding},
|
||
author={Abdelrahman Shaker and Muhammad Maaz and Chenhui Gou and Hamid Rezatofighi and Salman Khan and Fahad Shahbaz Khan},
|
||
year={2026},
|
||
eprint={2503.21782},
|
||
archivePrefix={arXiv},
|
||
primaryClass={cs.CV},
|
||
url={https://arxiv.org/abs/2503.21782},
|
||
}
|
||
|
||
|
||
@InProceedings{brohan_rt2_2023,
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title = {RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control},
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author = {Zitkovich, Brianna and Yu, Tianhe and Xu, Sichun and Xu, Peng and Xiao, Ted and others},
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booktitle = {Proceedings of The 7th Conference on Robot Learning},
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pages = {2165--2183},
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year = {2023},
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editor = {Tan, Jie and Toussaint, Marc and Darvish, Kourosh},
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volume = {229},
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series = {Proceedings of Machine Learning Research},
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month = {Nov},
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publisher = {PMLR}
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}
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@InProceedings{kim_openvla_2024,
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title = {OpenVLA: An Open-Source Vision-Language-Action Model},
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author = {Kim, Moo Jin and Pertsch, Karl and Karamcheti, Siddharth and Xiao, Ted and others},
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booktitle = {Proceedings of The 8th Conference on Robot Learning},
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pages = {2679--2713},
|
||
year = {2025},
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editor = {Agrawal, Pulkit and Kroemer, Oliver and Burgard, Wolfram},
|
||
volume = {270},
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||
series = {Proceedings of Machine Learning Research},
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||
month = {Nov},
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publisher = {PMLR},
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}
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@INPROCEEDINGS{khani_adaptive_2021,
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author={Khani, Mehrdad and Hamadanian, Pouya and Nasr-Esfahany, Arash and Alizadeh, Mohammad},
|
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booktitle={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
|
||
title={Real-Time Video Inference on Edge Devices via Adaptive Model Streaming},
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||
year={2021},
|
||
volume={},
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||
number={},
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pages={4552-4562},
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||
doi={10.1109/ICCV48922.2021.00453}}
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||
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@INPROCEEDINGS{wang_runespoor_2021,
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author={Wang, Yiding and Wang, Weiyan and Liu, Duowen and Jin, Xin and Jiang, Junchen and Chen, Kai},
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booktitle={IEEE INFOCOM 2021 - IEEE Conference on Computer Communications},
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||
title={Enabling Edge-Cloud Video Analytics for Robotics Applications},
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||
year={2021},
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volume={},
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number={},
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pages={1-10},
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doi={10.1109/INFOCOM42981.2021.9488801}}
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||
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||
@inproceedings{fu_video-mme_2025,
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title={Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis},
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author={Fu, Chaoyou and Dai, Yuhan and Luo, Yongdong and Li, Lei and Ren, Shuhuai and Zhang, Renrui and Wang, Zihan and Zhou, Chenyu and Shen, Yunhang and Zhang, Mengdan and others},
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booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
|
||
pages={24108--24118},
|
||
year={2025}
|
||
}
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||
|
||
@article{wu_longvideobench_2024,
|
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title={Longvideobench: A benchmark for long-context interleaved video-language understanding},
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author={Wu, Haoning and Li, Dongxu and Chen, Bei and Li, Junnan},
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journal={Advances in Neural Information Processing Systems},
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volume={37},
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pages={28828--28857},
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year={2024}
|
||
}
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||
|
||
@inproceedings{zhou_mlvu_2025,
|
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title={Mlvu: Benchmarking multi-task long video understanding},
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author={Zhou, Junjie and Shu, Yan and Zhao, Bo and Wu, Boya and Liang, Zhengyang and Xiao, Shitao and Qin, Minghao and Yang, Xi and Xiong, Yongping and Zhang, Bo and others},
|
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
|
||
pages={13691--13701},
|
||
year={2025}
|
||
}
|
||
|
||
@inproceedings{huang_ovbench_2025,
|
||
title={Online video understanding: Ovbench and videochat-online},
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||
author={Huang, Zhenpeng and Li, Xinhao and Li, Jiaqi and Wang, Jing and Zeng, Xiangyu and Liang, Cheng and Wu, Tao and Chen, Xi and Li, Liang and Wang, Limin},
|
||
booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
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pages={3328--3338},
|
||
year={2025}
|
||
}
|
||
|
||
@inproceedings{lin_streamingbench_2024,
|
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title={Streamingbench: Assessing the gap for mllms to achieve streaming video understanding},
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author={Lin, Junming and Fang, Zheng and Chen, Chi and Cheng, Haoxuan and Wan, Zihao and Luo, Fuwen and Wang, Ziyue and Li, Peng and Liu, Yang and Sun, Maosong},
|
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booktitle={ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
|
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pages={12147--12151},
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year={2026},
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organization={IEEE}
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||
}
|
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@inproceedings{yang_egolife_2025,
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title={Egolife: Towards egocentric life assistant},
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author={Yang, Jingkang and Liu, Shuai and Guo, Hongming and Dong, Yuhao and Zhang, Xiamengwei and Zhang, Sicheng and Wang, Pengyun and Zhou, Zitang and Xie, Binzhu and Wang, Ziyue and others},
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booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
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pages={28885--28900},
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year={2025}
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}
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|
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@article{FJDN202504015,
|
||
author = { 钟育伙 },
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title = {WebRTC的实时会议系统设计与实现},
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journal = {福建电脑},
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pages = {80-84},
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doi = {10.16707/j.cnki.fjpc.2025.04.015}
|
||
}
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@techreport{huawei2025wireless,
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author = {{Huawei Technologies Co., Ltd.}},
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title = {Striding Towards the Intelligent World White Paper: Wireless Network},
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institution = {Huawei},
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year = {2025},
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url = {https://www-file.huawei.com/admin/asset/v1/pro/view/9a6d45ad388446e29a1ad513b1b30788.pdf}
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}
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@techreport{IEEE_HUAWEI_Post_bn,
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author = {{Huawei Technologies Co., Ltd.}},
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title = {Post-802.11bn},
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institution = {HUAWEI},
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number = {11-26-0180-00-0wng},
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year = {2026},
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month = {Jan.},
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type = {IEEE 802.11 WNG Contribution},
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url = {https://mentor.ieee.org/802.11/dcn/26/11-26-0180-00-0wng-post-802-11bn.pptx}
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}
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@techreport{IEEE_HUAWEI_bn_for_ai,
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author = {{Huawei Technologies Co., Ltd.}},
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title = {802.11bx for AI},
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institution = {HUAWEI},
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year = {2026},
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month = {May.},
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type = {IEEE 802.11 WNG Contribution},
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url = {https://mentor.ieee.org/802.11/dcn/26/11-26-1012-00-0wng-802-11bx-for-ai.pptx}
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}
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@techreport{IEEE_80211_AIO_SG_agenda,
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author = {{IEEE 802.11 AI Offload Study Group}},
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institution = {IEEE 802.11},
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month = {May.},
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type = {IEEE 802.11 AIO SG Contribution},
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url = {https://mentor.ieee.org/802.11/dcn/26/11-26-0779-04-0aio-aio-sg-may-2026-meeting-agenda.pptx}
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url = {https://www.usenix.org/conference/hotcloud18/presentation/pakha},
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series = {SIGCOMM '18}
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}
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@inproceedings{jiang2018chameleon,
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year = {2018},
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@INPROCEEDINGS{zhang2022casva,
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author={Zhang, Miao and Wang, Fangxin and Liu, Jiangchuan},
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number={},
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pages={2168-2177},
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keywords={Deep learning;Visual analytics;Neural networks;Bandwidth;Reinforcement learning;Streaming media;Cameras},
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doi={10.1109/INFOCOM48880.2022.9796875}}
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@inproceedings{wu2025chatwithai,
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title = {Chat with AI: The Surprising Turn of Real-time Video Communication from Human to AI},
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year = {2025},
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isbn = {9798400722806},
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publisher = {Association for Computing Machinery},
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url = {https://doi.org/10.1145/3772356.3772390},
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booktitle = {Proceedings of the 24th ACM Workshop on Hot Topics in Networks},
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pages = {402–410},
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numpages = {9},
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location = {UMD Campus, College Park, MD, USA},
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series = {HotNets '25}
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}
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|
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@misc{wu2026artic,
|
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title={Artic: AI-oriented Real-time Communication for MLLM Video Assistant},
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author={Jiangkai Wu and Zhiyuan Ren and Junquan Zhong and Liming Liu and Xinggong Zhang},
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year={2026},
|
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eprint={2602.12641},
|
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archivePrefix={arXiv},
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primaryClass={cs.NI},
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url={https://arxiv.org/abs/2602.12641},
|
||
}
|