  With the increasing demands on interactive video applications, how to adapt
video bit rate to avoid network congestion has become critical, since
congestion results in self-inflicted delay and packet loss which deteriorate
the quality of real-time video service. 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 reveal that
congestion delay is determined by sending rate, receiving rate and network
status, 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 the 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 of transport layer (UDP) and application layer (QUIC)
respectively. The testing environment includes emulated network, real-world
Internet and commercial LTE networks. Compared against TCP flavors and
state-of-the-art protocols, Iris is able to achieve high bandwidth utilization,
low latency and good fairness concurrently. Especially over QUIC, Iris is able
to increase the video bitrate up to 25%, and PSNR up to 1dB.
