# 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）