TrafAda: Cost-Aware Traffic Adaptation for Maximizing Bitrates in Live Streaming

The business growth of live streaming causes expensive bandwidth costs from the Content Delivery Network service. It necessitates traffic adaptation, i.e., adapting video bitrates for cost-efficient bandwidth utilization, especially under the 95 $^{\rm \textit {th}}$ percentile pricing. However, our data-driven investigations indicate the existing methods are hard to achieve bitrate-cost balance in a long month-level billing cycle due to dynamic traffic patterns. We propose TrafAda, a learning-based cost-aware traffic adaptation method consisting of i) an ultra-long-term bandwidth demand forecasting model to learn complex bandwidth usage patterns, and ii) an imitation learning-based bitrate decision mechanism to optimize the ultra-long-term objective. We have implemented and deployed TrafAda on a large-scale live streaming system in China serving over one billion viewers from 388 cities. The results show that TrafAda improves peak-hour bitrate, quality of experience (QoE), and watching time by 34.75%, 44.56%, and 10.68%, respectively, without extra bandwidth cost, which can be converted to a considerable value for a commercial system.