The Impact of Mobile Device Preference on the Quality of Experience

Elmnsi, Abubkr; Osman, Niemah I.; Mkwawa, Is-Haka

doi:10.21700/ijcis.2016.112

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Even though 5G networks promise high connectivity and huge transmission capacity aiming to take the internet services and the corresponding user experience to the next level [3], [4], bandwidth sharing is still an important issue for network operators and content providers, especially in view of the exponential rise of video traffic volume ( Figure 1). It turns out that the objective aspect of Quality of Experience is tightly correlated to terminal playout characteristics (e.g., size, resolution) but also to network conditions [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

On accounting for screen resolution in adaptive video streaming: QoE‐driven bandwidth sharing framework

2020

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Screen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. User terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience. Previous studies tried to link MOS (Mean Opinion Score) to video bitrate for different screen types (e.g., Common Intermediate Format (CIF), Quarter Common Intermediate Format (QCIF), and High Definition (HD)). We leverage such studies and formulate a QoE driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE (Quality of Experience) over all users of a network service provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on datasets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and KKT (Karush Kuhn Tucker) conditions to efficiently solve the optimization problem. Our numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with a TCP look-alike strategy implementing max-min fairness.

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Section: Introductionmentioning

confidence: 99%

On accounting for screen resolution in adaptive video streaming: QoE‐driven bandwidth sharing framework

2020

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“…Even though 5G networks promise high connectivity and huge transmission capacity aiming to take users experience to the next level [5], [6], bandwidth sharing is still an important issue for network operators and content providers, especially in view of the exponential rise of video traffic volume. It turns out that the objective aspect of Quality of Experience is tightly correlated to terminal playout characteristics (e.g, size, resolution) but also to network conditions [1], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

On Accounting for Screen Resolution in Adaptive Video Streaming: A QoE-Driven Bandwidth Sharing Framework

Belmoukadam¹,

Khokhar²,

Barakat³

2019

2019 15th International Conference on Network and Service Management (CNSM)

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Screen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. Terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience [1]. Previous studies tried to link MOS (Mean Opinion Score) to video bit rate for different screen types (e.g., CIF, QCIF, and HD) [2]. We leverage such studies and formulate a QoE-driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE (Quality of Experience) over all users of a provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on data sets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and KKT (Karush Kuhn Tucker) conditions for a subset of constraints. Numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with TCP look-alike strategies implementing max-min fairness. Later, we use a MPEG/DASH implementation in the context of ns-3 and show that coupling our approach with a rate adaptation algorithm (e.g., [3]) can help increasing QoE while reducing both resolution switches and number of interruptions.

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The Impact of Mobile Device Preference on the Quality of Experience

Cited by 2 publications

References 14 publications

On accounting for screen resolution in adaptive video streaming: QoE‐driven bandwidth sharing framework

On accounting for screen resolution in adaptive video streaming: QoE‐driven bandwidth sharing framework

On Accounting for Screen Resolution in Adaptive Video Streaming: A QoE-Driven Bandwidth Sharing Framework

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