DAM: Deep Reinforcement Learning based Preload Algorithm with Action Masking for Short Video Streaming

Short video streaming has been increasingly popular in recent years. Due to its unique user behavior of watching and sliding, a critical technique issue is to design a preload algorithm deciding which video chunk to download next, bitrate selection and the pause time, in order to improve user experience while reducing bandwidth wastage. However, designing such a preload algorithm is non-trivial, especially taking into account conflicting goals of improving QoE and reducing bandwidth wastage. In this paper, we propose a deep reinforcement learning-based approach to simultaneously decide the aforementioned three decision variables via learning an optimal policy under a complex environment of varying network conditions and unpredictable user behavior. In particular, we incorporate domain knowledge into the decision procedure via action masking to make decisions more transparent, and accelerate the model training. Experimental results validate the proposed approach significantly outperforms baseline algorithms in terms of QoE metrics and bandwidth wastage.