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

Long Minh Luu, Nghia Trung Nguyen, Phuong Luu Vo, Tuan-Anh Le


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, 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.

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