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.
Dynamic adaptive streaming over HTTP (DASH) has been widely used in video
streaming recently. In DASH, the client downloads video chunks in order from
a server. The rate adaptation function at the video client enhances the
user?s quality-of-experience (QoE) by choosing a suitable quality level for
each video chunk to download based on the network condition. Today networks
such as content delivery networks, edge caching networks, content centric
networks, etc. usually replicate video contents on multiple cache nodes. We
study video streaming from multiple sources in this work. In multi-source
streaming, video chunks may arrive out of order due to different conditions
of the network paths. Hence, to guarantee a high QoE, the video client needs
not only rate adaptation, but also chunk scheduling. Reinforcement learning
(RL) has emerged as the state-of-the-art control method in various fields
in recent years. This paper proposes two algorithms for streaming from
multiple sources: RL-based adaptation with greedy scheduling (RLAGS) and
RL-based adaptation and scheduling (RLAS). We also build a simulation
environment for training and evaluation. The efficiency of the proposed
algorithms is proved via extensive simulations with real-trace data.
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