3.9 KiB
3.9 KiB
国内外研究现状
一、实时视觉大模型通信需求研究
- 1. 流式视频理解与在线响应
- VideoLLM-online: Online Video Large Language Model for Streaming Video（2024）
- LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval（2025）
- CodecSight: Leveraging Video Codec Signals for Efficient Streaming VLM Inference（2026）
- 2. 在线视频理解评测基准
- OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?（2025）
- StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding（2026）
- 3. 边缘/具身智能应用
- Vision-Language Models on the Edge for Real-Time Robotic Perception（2026）
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control（2023）
- OpenVLA: An Open-Source Vision-Language-Action Model（2025）
- EgoLife: Towards Egocentric Life Assistant（2025）
二、面向视频类 AI 的视频传输优化研究
- 1. 面向视频分析的自适应传输
- Reinventing Video Streaming for Distributed Vision Analytics（2018）
- AWStream: Adaptive Wide-Area Streaming Analytics（2018）
- Chameleon: Scalable Adaptation of Video Analytics（2018）
- 2. 边缘视频分析与模型反馈驱动传输
- Server-Driven Video Streaming for Deep Learning Inference（2020）
- Enabling Edge-Cloud Video Analytics for Robotics Applications（2021）
- CASVA: Configuration-Adaptive Streaming for Live Video Analytics（2022）
- 3. 面向多模态大模型的视频通信优化
- Chat with AI: The Surprising Turn of Real-Time Video Communication from Human to AI（2025）
- ARtIC: AI-Oriented Real-Time Communication for MLLM Video Assistant（2026）
三、WebRTC 优化研究
- 1. 端到端拥塞控制
- Making Google Congestion Control Robust over Wi-Fi Networks Using Packet Grouping（2016）
- Congestion Control for Web Real-Time Communication（2017）
- Learning-Based Congestion Control for Internet Video Communication over Wireless Networks（2018）
- Statistical Learning Based Congestion Control for Real-Time Video Communication（2020）
- 2. 动态网络下的速率控制
- Vidaptive: Efficient and Responsive Rate Control for Real-Time Video on Variable Networks（2024）
- Mustang: Improving QoE for Real-Time Video in Cellular Networks by Masking Jitter（2024）
- Mortise: Auto-Tuning Congestion Control to Optimize QoE via Network-Aware Parameter Optimization（2026）
- 3. 多维视频 QoE 与 FEC 优化
- R-FEC: RL-Based FEC Adjustment for Better QoE in WebRTC（2022）
- Mamba: Bringing Multi-Dimensional ABR to WebRTC（2023）
- ABRF: Adaptive Bitrate-FEC Joint Control for Real-Time Video Streaming（2023）
- Real-Time Rate Control of WebRTC Video Streams in 5G Networks: Improving Quality of Experience with Deep Reinforcement Learning（2024）
四、Wi-Fi 实时业务优化研究
- 1. Wi-Fi 实时业务 QoS 与低时延机制
- Tuning Channel Access to Enable Real-Time Applications in Wi-Fi 7（2020）
- Time-Sensitive Networking in IEEE 802.11be: On the Way to Low-Latency WiFi 7（2021）
- 2. Wi-Fi 配置与实时视频 QoE 测量
- Understanding the Impact of Wi-Fi Configuration on Volumetric Video Streaming Applications（2023）
- Experimental Evaluation of Interactive Edge/Cloud Virtual Reality Gaming over Wi-Fi Using Unity Render Streaming（2024）
- 3. Wi-Fi 状态辅助应用层传输控制
- Revisiting Congestion Control for WiFi Networks（2024）
- Athena: Seeing and Mitigating Wireless Impact on Video Conferencing and Beyond（2024）
- 4. 应用层信息辅助 Wi-Fi 调度
- Flick: Frame-Perceptive Packet Scheduling for Low-Latency Video Services in Wi-Fi Networks（2025）
- LAW: Towards Consistent Low Latency in 802.11 Home Networks（2026）
- BLADE: Adaptive Wi-Fi Contention Control for Next-Generation Real-Time Communication（2026）