Congestion Control Optimization for Short Video Services: User-End and Edge Server Collaboration in Practice

Short video applications such as TikTok, Douyin, and Kwai have experienced significant popularity in recent years. However, the quality of experience (QoE) provided by short video streaming services still falls short of expectations. As a leading provider of short video services with proprietary video players and content delivery network (CDN) capabilities, we are in a unique position to optimize the QoE of these services. In this study, we present our pilot investigation into congestion control performance optimization for short video services by leveraging collaboration between user-end video players and edge servers. Based on a comprehensive measurement study of network characteristics from production networks and incorporating feedback from video players, we developed an optimized congestion control algorithm called BBR-E2E. We deployed BBR-E2E in our production network and conducted a large-scale A/B testing across the country, involving trillions of video sessions over a three-month period in China. Overall, we observed a 1.6% reduction in rebuffering duration and a 6.2% decrease in rebuffering count. At the provincial level¹¹A province in China is similar to a state in the USA., the improvements were even more substantial, with up to a 7.8% reduction in rebuffering duration and a 13.7% decrease in rebuffering count.