Statistical Learning Based Congestion Control for Real-Time Video Communication

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.