cc 15(4): e1

Research Article

A QoE Aware Fairness Bi-level Resource Allocation Algorithm for Multiple Video Streaming in WLAN

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  • @ARTICLE{10.4108/eai.19-8-2015.2259765,
        author={Hu Zhou and Bo Li and Zhongjiang Yan and Xiaoya Zuo and Mao Yang},
        title={A QoE Aware Fairness Bi-level Resource Allocation Algorithm for Multiple Video Streaming in WLAN},
        journal={EAI Endorsed Transactions on Collaborative Computing},
        volume={1},
        number={4},
        publisher={EAI},
        journal_a={CC},
        year={2015},
        month={9},
        keywords={quality of the experience (qoe), fairness, resource allocation, http video streaming, wireless local area network (wlan)},
        doi={10.4108/eai.19-8-2015.2259765}
    }
    
  • Hu Zhou
    Bo Li
    Zhongjiang Yan
    Xiaoya Zuo
    Mao Yang
    Year: 2015
    A QoE Aware Fairness Bi-level Resource Allocation Algorithm for Multiple Video Streaming in WLAN
    CC
    EAI
    DOI: 10.4108/eai.19-8-2015.2259765
Hu Zhou1, Bo Li1, Zhongjiang Yan1,*, Xiaoya Zuo1, Mao Yang1
  • 1: School of Electronics and Information Northwestern Polytechnical University, Xi’an, China
*Contact email: zhjyan@nwpu.edu.cn

Abstract

With the increasing of smart devices such as mobile phones and tablets, the scenario of multiple video users watching video streaming simultaneously in one wireless local area network (WLAN) becomes more and more popular. However, the quality of experience (QoE) and the fairness among multiple users are seriously impacted by the limited bandwidth and shared resources of WLAN. In this paper, we propose a novel bi-level resource allocation algorithm. To maximize the total throughput of the network, the WLAN is firstly tuned to the optimal operation point. Then the wireless resource is carefully allocated at the first level, i.e., between AP and uplink background traffic users, and the second level, i.e., among downlink video users. The simulation results show that the proposed algorithm can guarantee the QoE and the fairness for all the video users, and there is little impact on the average throughput of the background traffic users.