AMIS-MU: Edge Computing Based Adaptive Video Streaming for Multiple Mobile Users

doi:10.1109/TMC.2022.3225414

AMIS-MU: Edge Computing Based Adaptive Video Streaming for Multiple Mobile Users

2024-01-01

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DOI：10.1109/TMC.2022.3225414

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The increasing demand for online high-quality video streaming has brought huge challenges to the traditional client-server video streaming systems due to the high feedback delay, rigorous bandwidth requirement, and the lack of a mechanism of centralized resource management between users. In this work, we propose AMIS-MU, an edge computing-based mobile video streaming system that optimizes the watching experience of users via playback adaptation and channel resource allocation. AMIS-MU fully explores the power of edge servers from three perspectives. First, by pre-caching videos from the cloud, AMIS-MU analyzes video contents at the edge, and achieves a nearly imperceptible content-based playback speed adaptation. Second, as the edge server controls the channel resources of users in a centralized fashion, AMIS-MU adaptively updates the channel configuration to optimize the overall watching experience. Last, the plenty of computational power available at the edge enables a more intelligent playback control by using deep reinforcement learning (DRL). We propose a novel usage of DRL which significantly reduces the complexity of the cross-layer joint optimization problem and solve the non-convex channel resource allocation problem by Lyapunov optimization. Experiments show that AMIS-MU outperforms other existing algorithms in terms of average QoE and fairness.

Keywords:

Adaptive video streaming

deep reinforcement learning

edge computing

channel resource allocation

OFDMA

AMIS-MU: Edge Computing Based Adaptive Video Streaming for Multiple Mobile Users

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