5G-QoE : QoE modelling for ultra-HD video streaming in 5G networks

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Nightingale, James and Salva-Garcia, Pablo and Calero, Jose M. Alcaraz and Wang, Qi (2018) 5G-QoE : QoE modelling for ultra-HD video streaming in 5G networks. IEEE Transactions on Broadcasting, 64 (2). pp. 621-634. ISSN 0018-9316 (https://doi.org/10.1109/TBC.2018.2816786)

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Abstract

Traffic on future fifth-generation (5G) mobile networks is predicted to be dominated by challenging video applications such as mobile broadcasting, remote surgery and augmented reality, demanding real-time, and ultra-high quality delivery. Two of the main expectations of 5G networks are that they will be able to handle ultra-high-definition (UHD) video streaming and that they will deliver services that meet the requirements of the end user's perceived quality by adopting quality of experience (QoE) aware network management approaches. This paper proposes a 5G-QoE framework to address the QoE modeling for UHD video flows in 5G networks. Particularly, it focuses on providing a QoE prediction model that is both sufficiently accurate and of low enough complexity to be employed as a continuous real-time indicator of the 'health' of video application flows at the scale required in future 5G networks. The model has been developed and implemented as part of the EU 5G PPP SELFNET autonomic management framework, where it provides a primary indicator of the likely perceptual quality of UHD video application flows traversing a realistic multi-tenanted 5G mobile edge network testbed. The proposed 5G-QoE framework has been implemented in the 5G testbed, and the high accuracy of QoE prediction has been validated through comparing the predicted QoE values with not only subjective testing results but also empirical measurements in the testbed. As such, 5G-QoE would enable a holistic video flow self-optimisation system employing the cutting-edge Scalable H.265 video encoding to transmit UHD video applications in a QoE-aware manner.

  • Item type:Article
    ID code:65207
    Dates:
    Date
    Event
    30 June 2018
    Published
    5 April 2018
    Published Online
    5 March 2018
    Accepted
    Notes:(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:17 Aug 2018 14:15
    Last modified:17 Dec 2024 04:41
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/65207
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