Reinforcement learning for bandwidth estimation and congestion control in real-time communications
This addresses the problem of improving quality of experience for users in audio and video conferencing, but it is incremental as it builds on existing research with a new method.
The paper tackled bandwidth estimation and congestion control in real-time communications by applying reinforcement learning for the first time to optimize user-perceived quality, achieving initial proof-of-concept results through network simulation and real Internet video calls.
Bandwidth estimation and congestion control for real-time communications (i.e., audio and video conferencing) remains a difficult problem, despite many years of research. Achieving high quality of experience (QoE) for end users requires continual updates due to changing network architectures and technologies. In this paper, we apply reinforcement learning for the first time to the problem of real-time communications (RTC), where we seek to optimize user-perceived quality. We present initial proof-of-concept results, where we learn an agent to control sending rate in an RTC system, evaluating using both network simulation and real Internet video calls. We discuss the challenges we observed, particularly in designing realistic reward functions that reflect QoE, and in bridging the gap between the training environment and real-world networks.