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

Authors:
raw_author_name: Yizong Wang
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology and the School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Dong Zhao
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology and the School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Chenghao Huang
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology and the School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Fuyu Yang
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology and the School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Teng Gao
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology and the School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Anfu Zhou
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology and the School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Huanhuan Zhang
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology and the School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Huadong Ma
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology and the School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Yang Du
raw_affiliation_string: Beijing, China
raw_author_name: Aiyun Chen
raw_affiliation_string: Shanghai, China

Abstract: The business growth of live streaming causes expensive bandwidth costs from the Content Delivery Network service. It necessitates traffic adaptation, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , adapting video bitrates for cost-efficient bandwidth utilization, especially under the 95 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>th</i> </sup> 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrafAda</i> , 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrafAda</i> on a large-scale live streaming system in China serving over one billion viewers from 388 cities. The results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrafAda</i> 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.