ALVS: Adaptive Live Video Streaming using deep reinforcement learning

Achieving a high Quality of Experience (QoE) in live event streaming is a challenging problem given a low-latency requirement and time-varying network conditions. Adaptive video bitrate and adaptive playback speed techniques are two separate control knobs to address this challenge. In this paper, we consider these two control parameters in a joint optimization problem and present a deep reinforcement learning (DRL) framework to maximize QoE for live streaming without any assumption about the environment or fixed rule-based heuristics. With the proposed DRL framework, our approach (ALVS) constructs the inference model to make a joint decision of adaptive playback speed and video quality level for the next video segment. Simulation results through real network traces show that ALVS outperforms both state-of-the-art DRL-based and rule-based algorithms in terms of QoE without sacrificing live latency and skipping any content.