QoE-aware optimization of video stream downlink scheduling over LTE networks using RNNs and genetic algorithm: The 11th International Conference on Future Networks and Communications (FNC 2016)

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    Abstract

    Long Term Evolution (LTE) is the initial version of fourth-generation (4G) networks which provides ubiquitous broadband access. LTE supports multimedia Quality of Service (QoS) traffic with high data transfer speed, fast communication connectivity, and high security. Multimedia traffic over LTE networks is one of the highest percentages of mobile traffic and it has been growing rapidly in recent years. Our approach focuses on the development of Quality of Experience (QoE) aware optimization downlink scheduling video traffic flow. QoE is the overall acceptability of a service or application, as perceived subjectively by end users. In this work we aim to maximise QoE of video traffic streaming over LTE networks. This work introduces a novel integration framework between genetic algorithm (GA) and random neural networks (RNN) applied to QoE-aware optimization of video stream downlink scheduling. The proposed framework has been applied and evaluated using an open source simulation tool for LTE networks (LTE-Sim). A comparison between our framework and state-of-the-art LTE downlink scheduling algorithms (FLS, EXP-rule, and LOG-rule) has been done under different network conditions. Simulation results have shown that our scheduler can achieve better performance in terms of QoE (~10% increase), throughput and fairness.
    Original languageEnglish
    Pages (from-to)232-239
    Number of pages8
    JournalProcedia Computer Science
    Volume94
    Early online date10 Aug 2016
    DOIs
    Publication statusPublished - Aug 2016

    Fingerprint

    Long Term Evolution (LTE)
    Genetic algorithms
    Scheduling
    Communication
    Data transfer
    Scheduling algorithms
    Telecommunication traffic
    Quality of service
    Throughput
    Neural networks

    Keywords

    • Long Term Evolution
    • 4G networks
    • quality of experience

    Cite this

    @article{770122371c5e417ab60a287e5c013396,
    title = "QoE-aware optimization of video stream downlink scheduling over LTE networks using RNNs and genetic algorithm: The 11th International Conference on Future Networks and Communications (FNC 2016)",
    abstract = "Long Term Evolution (LTE) is the initial version of fourth-generation (4G) networks which provides ubiquitous broadband access. LTE supports multimedia Quality of Service (QoS) traffic with high data transfer speed, fast communication connectivity, and high security. Multimedia traffic over LTE networks is one of the highest percentages of mobile traffic and it has been growing rapidly in recent years. Our approach focuses on the development of Quality of Experience (QoE) aware optimization downlink scheduling video traffic flow. QoE is the overall acceptability of a service or application, as perceived subjectively by end users. In this work we aim to maximise QoE of video traffic streaming over LTE networks. This work introduces a novel integration framework between genetic algorithm (GA) and random neural networks (RNN) applied to QoE-aware optimization of video stream downlink scheduling. The proposed framework has been applied and evaluated using an open source simulation tool for LTE networks (LTE-Sim). A comparison between our framework and state-of-the-art LTE downlink scheduling algorithms (FLS, EXP-rule, and LOG-rule) has been done under different network conditions. Simulation results have shown that our scheduler can achieve better performance in terms of QoE (~10{\%} increase), throughput and fairness.",
    keywords = "Long Term Evolution, 4G networks, quality of experience",
    author = "Tarik Ghalut and Hadi Larijani and Ali Shahrabi",
    note = "Accepted: 14-5-16 Online pub: 10-8-16 OA article, no funder note Duplicate record created by HL 23-3-17, no FT etc.",
    year = "2016",
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    language = "English",
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    T1 - QoE-aware optimization of video stream downlink scheduling over LTE networks using RNNs and genetic algorithm

    T2 - The 11th International Conference on Future Networks and Communications (FNC 2016)

    AU - Ghalut, Tarik

    AU - Larijani, Hadi

    AU - Shahrabi, Ali

    N1 - Accepted: 14-5-16 Online pub: 10-8-16 OA article, no funder note Duplicate record created by HL 23-3-17, no FT etc.

    PY - 2016/8

    Y1 - 2016/8

    N2 - Long Term Evolution (LTE) is the initial version of fourth-generation (4G) networks which provides ubiquitous broadband access. LTE supports multimedia Quality of Service (QoS) traffic with high data transfer speed, fast communication connectivity, and high security. Multimedia traffic over LTE networks is one of the highest percentages of mobile traffic and it has been growing rapidly in recent years. Our approach focuses on the development of Quality of Experience (QoE) aware optimization downlink scheduling video traffic flow. QoE is the overall acceptability of a service or application, as perceived subjectively by end users. In this work we aim to maximise QoE of video traffic streaming over LTE networks. This work introduces a novel integration framework between genetic algorithm (GA) and random neural networks (RNN) applied to QoE-aware optimization of video stream downlink scheduling. The proposed framework has been applied and evaluated using an open source simulation tool for LTE networks (LTE-Sim). A comparison between our framework and state-of-the-art LTE downlink scheduling algorithms (FLS, EXP-rule, and LOG-rule) has been done under different network conditions. Simulation results have shown that our scheduler can achieve better performance in terms of QoE (~10% increase), throughput and fairness.

    AB - Long Term Evolution (LTE) is the initial version of fourth-generation (4G) networks which provides ubiquitous broadband access. LTE supports multimedia Quality of Service (QoS) traffic with high data transfer speed, fast communication connectivity, and high security. Multimedia traffic over LTE networks is one of the highest percentages of mobile traffic and it has been growing rapidly in recent years. Our approach focuses on the development of Quality of Experience (QoE) aware optimization downlink scheduling video traffic flow. QoE is the overall acceptability of a service or application, as perceived subjectively by end users. In this work we aim to maximise QoE of video traffic streaming over LTE networks. This work introduces a novel integration framework between genetic algorithm (GA) and random neural networks (RNN) applied to QoE-aware optimization of video stream downlink scheduling. The proposed framework has been applied and evaluated using an open source simulation tool for LTE networks (LTE-Sim). A comparison between our framework and state-of-the-art LTE downlink scheduling algorithms (FLS, EXP-rule, and LOG-rule) has been done under different network conditions. Simulation results have shown that our scheduler can achieve better performance in terms of QoE (~10% increase), throughput and fairness.

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