QoE-Driven In-Network Optimization for Adaptive Video Streaming Based on Packet Sampling Measurements

Niels Bouten, R. de Oliveira Schmidt, Jeroen Famaey, Steven Latré, Aiko Pras, Filip De Turck

    Research output: Contribution to journalArticleAcademicpeer-review

    23 Citations (Scopus)

    Abstract

    HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive streaming solutions. In HAS, a video is temporally split into segments which are encoded at different quality rates. The client can then autonomously decide, based on the current buffer filling and network conditions, which quality representation it will download. Each of these players strives to optimize their individual quality, which leads to bandwidth competition, causing quality oscillations and buffer starvations. This article proposes a solution to alleviate these problems by deploying in-network quality optimization agents, which monitor the available throughput using sampling-based measurement techniques and optimize the quality of each client, based on a HAS Quality of Experience (QoE) metric. This in-network optimization is achieved by solving a linear optimization problem both using centralized as well as distributed algorithms. The proposed hybrid QoE-driven approach allows the client to take into account the in-network decisions during the rate adaptation process, while still keeping the ability to react to sudden bandwidth fluctuations in the local network. The proposed approach allows improving existing autonomous quality selection heuristics by at least 30%, while outperforming an in-network approach using purely bitrate-driven optimization by up to 19%.
    Original languageUndefined
    Pages (from-to)96-115
    Number of pages20
    JournalComputer networks
    Volume81
    DOIs
    Publication statusPublished - 22 Apr 2015

    Keywords

    • EWI-25860
    • Quality of Experience
    • Optimization
    • IR-95589
    • Sampling-based measurements
    • METIS-312522
    • Adaptive Video Streaming

    Cite this

    Bouten, Niels ; de Oliveira Schmidt, R. ; Famaey, Jeroen ; Latré, Steven ; Pras, Aiko ; De Turck, Filip. / QoE-Driven In-Network Optimization for Adaptive Video Streaming Based on Packet Sampling Measurements. In: Computer networks. 2015 ; Vol. 81. pp. 96-115.
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    abstract = "HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive streaming solutions. In HAS, a video is temporally split into segments which are encoded at different quality rates. The client can then autonomously decide, based on the current buffer filling and network conditions, which quality representation it will download. Each of these players strives to optimize their individual quality, which leads to bandwidth competition, causing quality oscillations and buffer starvations. This article proposes a solution to alleviate these problems by deploying in-network quality optimization agents, which monitor the available throughput using sampling-based measurement techniques and optimize the quality of each client, based on a HAS Quality of Experience (QoE) metric. This in-network optimization is achieved by solving a linear optimization problem both using centralized as well as distributed algorithms. The proposed hybrid QoE-driven approach allows the client to take into account the in-network decisions during the rate adaptation process, while still keeping the ability to react to sudden bandwidth fluctuations in the local network. The proposed approach allows improving existing autonomous quality selection heuristics by at least 30{\%}, while outperforming an in-network approach using purely bitrate-driven optimization by up to 19{\%}.",
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    author = "Niels Bouten and {de Oliveira Schmidt}, R. and Jeroen Famaey and Steven Latr{\'e} and Aiko Pras and {De Turck}, Filip",
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    QoE-Driven In-Network Optimization for Adaptive Video Streaming Based on Packet Sampling Measurements. / Bouten, Niels; de Oliveira Schmidt, R.; Famaey, Jeroen; Latré, Steven; Pras, Aiko; De Turck, Filip.

    In: Computer networks, Vol. 81, 22.04.2015, p. 96-115.

    Research output: Contribution to journalArticleAcademicpeer-review

    TY - JOUR

    T1 - QoE-Driven In-Network Optimization for Adaptive Video Streaming Based on Packet Sampling Measurements

    AU - Bouten, Niels

    AU - de Oliveira Schmidt, R.

    AU - Famaey, Jeroen

    AU - Latré, Steven

    AU - Pras, Aiko

    AU - De Turck, Filip

    N1 - eemcs-eprint-25860

    PY - 2015/4/22

    Y1 - 2015/4/22

    N2 - HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive streaming solutions. In HAS, a video is temporally split into segments which are encoded at different quality rates. The client can then autonomously decide, based on the current buffer filling and network conditions, which quality representation it will download. Each of these players strives to optimize their individual quality, which leads to bandwidth competition, causing quality oscillations and buffer starvations. This article proposes a solution to alleviate these problems by deploying in-network quality optimization agents, which monitor the available throughput using sampling-based measurement techniques and optimize the quality of each client, based on a HAS Quality of Experience (QoE) metric. This in-network optimization is achieved by solving a linear optimization problem both using centralized as well as distributed algorithms. The proposed hybrid QoE-driven approach allows the client to take into account the in-network decisions during the rate adaptation process, while still keeping the ability to react to sudden bandwidth fluctuations in the local network. The proposed approach allows improving existing autonomous quality selection heuristics by at least 30%, while outperforming an in-network approach using purely bitrate-driven optimization by up to 19%.

    AB - HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive streaming solutions. In HAS, a video is temporally split into segments which are encoded at different quality rates. The client can then autonomously decide, based on the current buffer filling and network conditions, which quality representation it will download. Each of these players strives to optimize their individual quality, which leads to bandwidth competition, causing quality oscillations and buffer starvations. This article proposes a solution to alleviate these problems by deploying in-network quality optimization agents, which monitor the available throughput using sampling-based measurement techniques and optimize the quality of each client, based on a HAS Quality of Experience (QoE) metric. This in-network optimization is achieved by solving a linear optimization problem both using centralized as well as distributed algorithms. The proposed hybrid QoE-driven approach allows the client to take into account the in-network decisions during the rate adaptation process, while still keeping the ability to react to sudden bandwidth fluctuations in the local network. The proposed approach allows improving existing autonomous quality selection heuristics by at least 30%, while outperforming an in-network approach using purely bitrate-driven optimization by up to 19%.

    KW - EWI-25860

    KW - Quality of Experience

    KW - Optimization

    KW - IR-95589

    KW - Sampling-based measurements

    KW - METIS-312522

    KW - Adaptive Video Streaming

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    JF - Computer networks

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