sesa 15(5): e3

Research Article

An Improved MOEA/D for QoS Oriented Multimedia Multicasting with Network Coding

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  • @ARTICLE{10.4108/icst.mobimedia.2015.259094,
        author={Zhaoyuan Wang and Huanlai Xing and Tianrui Li and Yan Yang and Rong Qu},
        title={An Improved MOEA/D for QoS Oriented Multimedia Multicasting with Network Coding},
        journal={EAI Endorsed Transactions on Security and Safety},
        volume={2},
        number={5},
        publisher={EAI},
        journal_a={SESA},
        year={2015},
        month={8},
        keywords={multicast, multimedia, multi-objective optimization, network coding, quality-of-service},
        doi={10.4108/icst.mobimedia.2015.259094}
    }
    
  • Zhaoyuan Wang
    Huanlai Xing
    Tianrui Li
    Yan Yang
    Rong Qu
    Year: 2015
    An Improved MOEA/D for QoS Oriented Multimedia Multicasting with Network Coding
    SESA
    EAI
    DOI: 10.4108/icst.mobimedia.2015.259094
Zhaoyuan Wang1, Huanlai Xing1,*, Tianrui Li1, Yan Yang1, Rong Qu2
  • 1: School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China
  • 2: School of Computer Science, The University of Nottingham, Nottingham, NG8 1BB, UK
*Contact email: hxx@home.swjtu.edu.cn

Abstract

Recent years witness a significant growth in multimedia applications. Among them, a stream of applications is real-time and requires one-to-many fast data transmission with stringent quality-of-service (QoS) requirements, where multicast is an important supporting technology. In particular, with more and more mobile end users requesting real-time broadband multimedia applications, it is of vital importance to provide them with satisfied quality of experience. As network coding can offer higher bandwidth to users and accommodate more flows for networks than traditional routing, this paper studies the multicast routing problem with network coding and formulates it as a multi-objective optimization problem. As delay and packet loss ratio (PLR) are two important performance indicators for QoS, we consider them as the two objectives for minimization. To address the problem above, we present a multi-objective evolutionary algorithm based on decomposition (MOEA/D), where an all population updating rule is devised to address the problem of lacking feasible solutions in the search space. Experimental results demonstrate the effectiveness of the proposed algorithm and it outperforms a number of state-of-the-art algorithms.