A Hybrid Receiver-side Congestion Control Scheme for Web Real-time Communication

Web real-time communication (WebRTC) employs congestion control to ensure the quality of experience (QoE). Different from congestion control schemes for TCP, WebRTC keeps a low-level playback buffer that considers excessively delayed packets as losses, which makes the congestion control for WebRTC more challenging. Existing heuristic schemes estimate the network conditions based on hand-crafted rules that may be suboptimal, leading to under-utilization or over-utilization of link capacity in many cases. On the other hand, the existing learning-based schemes train a model that acts in a large action space, which is hard to converge to a stable status and has low performance over unpredictable network conditions. In this paper, we propose a hybrid receiver-side congestion control (HRCC) framework, which combines a heuristic congestion control scheme with an RL-Agent that periodically generates a gain coefficient to tune the bandwidth estimated by the heuristic scheme. Extensive simulation experiments demonstrate that the HRCC's RL-Agent effectively tunes the bandwidth estimate of the heuristic scheme. The hybrid scheme achieves higher bandwidth utilization than the fully heuristic scheme with similar queuing delay and packet loss and outperforms the fully RL-based scheme on overall performance.