COCKTAIL: Video streaming QoE optimization with chunk replacement and guided learning

Abstract

Adaptive bitrate (ABR) algorithms aim to provide the best user quality of experience (QoE) under fluctuating operating environments. Prior ABR protocols address the QoE maximization problem with a plethora of approximate optimization techniques including model predictive control (MPC), Lyapunov optimization, and deep reinforcement learning (DRL). Even though these algorithms provide adequate performances, they focus only on bitrate selections, precluding the chunk replacement option. We proclaim that chunk replacement can enhance the QoE if duplicated downloading is carefully administered. Moreover, we point out that bitrate selection and chunk replacement should be closely coupled into the optimization problem to fully realize the potential of chunk replacement. We first formulate a novel optimization problem with an expanded decision space that encompasses chunk replacement as well as bitrate selection. We then propose COCKTAIL, a DRL-based ABR algorithm that discovers efficient solutions to the new optimization problem by using several learning techniques. Experiments on real-world network traces show that COCKTAIL outperforms state-of-the-art baselines with improvements up to 16.1% in average QoE.