期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 21, 期 5, 页码 3208-3221出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2021.3119401
关键词
Streaming media; Quality of experience; Resource management; Wireless communication; Bit rate; Transcoding; Bandwidth; Adaptive video streaming; mobile edge computing; resource allocation; wireless networks; online optimization
资金
- National Natural Science Foundation of China [62101336, 61871271]
- Tencent Rhinoceros Birds Scientific Research Foundation for Young Teachers of Shenzhen University from the Research Grants Council of Hong Kong [14208017, 14201920]
- Guangdong Province Pearl River Scholar Funding Scheme
- Key Project of Department of Education of Guangdong Province [2020ZDZX3050]
- National Key Research and Development Program [2019YFB1803305]
In this paper, we propose an online joint transcoding and transmission resource allocation algorithm for a heterogeneous multi-user MEC-enabled video streaming network with time-varying wireless channels. The algorithm maximizes the QoE of ABR streaming users while considering bandwidth and CPU constraints. By introducing queueing model constraints and decoupling the multi-stage problem, we propose a low-complexity online algorithm that provides additional QoE compared to state-of-the-art approaches.
The emerging mobile edge computing (MEC) technology has been recently applied to improve adaptive bitrate (ABR) streaming service quality under time-varying wireless channels. In this paper, we consider a heterogeneous multi-user MEC-enabled video streaming network with time-varying wireless channels in sequential time frames. In particular, we aim to design an online joint transcoding and transmission resource allocation algorithm to maximize the ABR streaming user's quality of experience (QoE) subject to the bandwidth and CPU constraints. The algorithm is online in the sense that the bitrate and resource allocation decisions made at each frame depend only on the observation of past events. We formulate the problem as a mixed integer non-linear programming (MINLP) that jointly determines bitrate adaptation, bandwidth allocation, and CPU cycle assignment. To cope with the challenge arising from the coupling decisions of adjacent frames, we propose a low-complexity online algorithm, named OCCA. Specifically, by introducing queueing model constraints, we transform the offline non-conex MINLP problem into a multi-frame problem. Then, we analytically decouple the multi-stage(frame) problem to multiple per-frame convex subproblems that can be solved with high robustness and low computational complexity. We perform simulations with realistic scenarios to evaluate the performance of the proposed algorithm. Results manifest that compared with state-of-the-art approaches, our proposed algorithm can provide 97.84% (on average) extra QoE.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据