Workshop on QoE-Aware Resource Allocation for Multimedia Communications

Research Article

QoE Metric Based Resource Allocation for Dynamic Adaptive Streaming over HTTP in LTE Networks

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  • @INPROCEEDINGS{10.4108/eai.13-7-2017.2270548,
        author={Xiao Zhang and Anyue Wang and Danpu Liu},
        title={QoE Metric Based Resource Allocation for Dynamic Adaptive Streaming over HTTP in LTE Networks},
        proceedings={Workshop on QoE-Aware Resource Allocation for Multimedia Communications},
        publisher={EAI},
        proceedings_a={QOE-RAMC},
        year={2017},
        month={12},
        keywords={dash qoe resource allocation algorithm lte},
        doi={10.4108/eai.13-7-2017.2270548}
    }
    
  • Xiao Zhang
    Anyue Wang
    Danpu Liu
    Year: 2017
    QoE Metric Based Resource Allocation for Dynamic Adaptive Streaming over HTTP in LTE Networks
    QOE-RAMC
    EAI
    DOI: 10.4108/eai.13-7-2017.2270548
Xiao Zhang1, Anyue Wang1, Danpu Liu1,*
  • 1: Beijing Laboratory of Advanced Information Network, Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, P.R. China, 100876
*Contact email: dpliu@bupt.edu.cn

Abstract

Dynamic Adaptive Streaming over HTTP (DASH) is the main business mode of video service because of its convenient deployment, low cost, and adaptability to different user requirements. Meanwhile Quality of Experience (QoE) has become the main evaluation indicator of video service quality. Considering users are required to periodically feedback QoE metric to base station in the MPEG-DASH protocol, a Resource Allocation algorithm based on QoE Metric Feedback (QMFRA) for LTE networks is proposed in this paper. QMFRA algorithm aims to maximize the weighted sum of all users’ data rates. The data rate reflects the user’s channel quality, while the weight represents the influence of QoE metric. We consider buffer level, the occurrence of stalling and switch in the design of the weights, in order to avoid stalling as far as possible and enhance user fairness in resource scheduling. Simulation results show that QMFRA algorithm can effectively improve user’s Mean Opinion Score (MOS) and reduce the occurrence of stalling, compared with the widely used Multi-Carrier Proportional Fair (MPF) scheduling.