Deep Reinforcement Learning based Bitrate Adaptations in Dynamic Adaptive Streaming over HTTP

Dynamic adaptive streaming over HTTP (DASH) has been a superior video streaming technology in recent years. Bitrate adaptation function at video player plays a vital role in guaranteeing a high quality-of-experience for the users. This work evaluates the performance of several advanced deep reinforcement learning algorithms, \textit{i.e.}, deep Q-learning, actor-critic, and proximal policy optimization, applied in bitrate adaptations and compares them with other rate adaptation methods with real-trace datasets.