The existing congestion control is hard to simultaneously achieve low latency, high throughput, good adaptability and fair bandwidth allocation, mainly because of the hardwired control strategy and egocentric convergence objective. To address these issues, we propose an end-to-end statistical learning based congestion control, named Iris. By exploring the underlying principles of self-inflicted delay, we find that RTT variation is linearly related to the difference between sending rate and receiving rate, which inspires us to control video bit rate using a statistical-learning congestion control model. The key idea of Iris is to force all flows to converge to the same queue load and adjust bit rate by the model. All flows keep a small and fixed number of packets queuing in the network, thus the fair bandwidth allocation and low latency are both achieved. Besides, the adjustment step size of sending rate is updated by online learning, to better adapt to dynamically changing networks. We carried out extensive experiments to evaluate the performance of Iris, with the implementations over transport layer and application layer respectively. The testing environment includes emulated network, real-world Internet and commercial cellular networks. Compared against Transmission Control Protocol (TCP) flavors and state-of-the-art protocols, Iris is able to achieve high bandwidth utilization, low latency and good fairness concurrently. Especially for HyperText Transfer Protocol (HTTP) video streaming service, Iris is able to increase the video bitrate up to 25% and Peak Signal to Noise Ratio (PSNR) up to 1 dB.

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.