A Congestion Control Algorithm for Live Video Streaming in Dynamic Network

Traditional TCP has been the dominant protocol for internet traffic after years of development in both academia and industry. However, the emergence of live video streaming applications and the increasing demand for low-latency video transmission, particularly in the context of sports and game live streaming, has posed a challenge to traditional TCP congestion control algorithms. As a representative TCP algorithm, BBR has been widely used in industry. However, BBR tends to inject more packets than the actual bottleneck in the dynamic network resulting in high latency. Because the maximum bandwidth in the past period is used to calculate the congestion window (CWND). To address this issue, we develop a recursive least squares (RLS) model to predict future bandwidth based on past bandwidth samples and update BBR's CWND periodically. To overcome the difficulty of modifying the kernel congestion control algorithm, we use extended Berkeley Packet Filter (eBPF) technology to rewrite TCP BBR and use eBPF MAP to exchange data between the kernel space and the data space. Experiments show that our algorithm can effectively reduce latency for live streaming in the dynamic network. Compared with CUBIC, our algorithm can achieve 76.9% average latency reduction with 2.3% average throughput loss and bring 42.4% average latency reduction with 1.9% average throughput loss compared with BBR.