PAR: Improving Video Bitrate Adaptation via Payload-Aware Throughput Prediction

Jialiang Pei, Congkai An, Anfu Zhou, Liang Liu, Huadong Ma

Abstract: Adaptive bitrate (ABR) algorithm is deployed extensively in commercial video delivery platforms, aiming to ensure users' quality of experience(QoE). Among the majority of existing ABR algorithms, throughput prediction plays a critical role. However, these predictors suffer from neglecting the throughput inconsistency across diverse chunk payloads under the network dynamics, e.g., the actual throughput of downloading a 4K or a 720P chunk is usually different, even when starting from the same moment. In this paper, we propose a payload-aware adaptive algorithm called PAR, which predicts multiple throughput estimations for different target payloads, and utilizes them to make better bitrate adaptation decisions. Trace-driven experiments show that PAR outperforms the existing ABR schemes across diverse network conditions, with the average QoE improvement of 2.66% to 79.43%.