Loading [MathJax]/extensions/TeX/extpfeil.js
AMIS-MU: Edge Computing Based Adaptive Video Streaming for Multiple Mobile Users | IEEE Journals & Magazine | IEEE Xplore

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

Publisher: IEEE

Abstract:

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 feedbac...View more

Abstract:

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.
Published in: IEEE Transactions on Mobile Computing ( Volume: 23, Issue: 1, January 2024)
Page(s): 117 - 134
Date of Publication: 29 November 2022

ISSN Information:

Publisher: IEEE

Funding Agency:


References

References is not available for this document.