Learning-based Congestion Control for Internet Video Communication over Wireless Networks

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 one-way 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 4.2× 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.