Impact of video resolution changes on QoE for adaptive video streaming

Asan, Avsar; Robitza, Werner; Mkwawa, Is-Haka; Sun, Lingfen; Ifeachor, Emmanuel; Raake, Alexander

doi:10.1109/icme.2017.8019297

Cited by 20 publications

(14 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As per prior subjective studies, the QoE of video streaming is a function of application layer QoS features that are either dependent on the video content (e.g., video bitrate) or the playout metrics (e.g., the intial startup delay) [7], [27]; the playout metrics further depend on the underlying network conditions such as the network throughput or delay. In this work, we consider building QoE functions that take as input the network throughput or the video bitrate, and differ to a future work the consideration of other factors that might also impact the QoE, though to a lesser importance, such as the delay and the loss rate.…”

Section: From Qos To Qoementioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

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.

show abstract

Section: From Qos To Qoementioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

show abstract

Section: From Qos To Qoementioning

confidence: 99%

“…We also compare the different implementations in terms of main application-level QoS factors (e.g., stalls, resolution switches and interruptions) that could impact the subjective QoE [8], [18]. We focus on the quality switches as they occur only in adaptive video streaming sessions making their evaluation important.…”

Section: A Simulating Qoe-driven Dashmentioning

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)

View full text Add to dashboard Cite

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.

show abstract

“…In the academic domain, researchers are now more inclined to use QoE to evaluate adaptive strategies by optimizing the HAS algorithms based on the different QoE models. Most of the related studies on improving the QoE of HAS are client-driven approaches, they obey the client-centric nature of HAS [ 11 , 12 , 13 , 14 ]. Some researches have also focused on the server or network side [ 15 , 16 , 17 ].…”

Section: Related Workmentioning

confidence: 99%

“…For the client-driven approaches, the research [ 11 ] presented the results of the impact of resolution changes on user-perceived QoE in HAS and further demonstrated that the content type and resolution change patterns have significant impacts on the perception of resolution changes. The findings may help develop better QoE models and adaptation mechanisms.…”

Section: Related Workmentioning

confidence: 99%

Enhancing QoE for Mobile Users by Environment-Aware HTTP Adaptive Streaming

Zhang

Shuyan

et al. 2018

Sensors

View full text Add to dashboard Cite

HTTP adaptive streaming (HAS) has become a dominated media streaming paradigm in today’s Internet, which enriches the user’s experience by matching the video quality with the dynamic network conditions. A range of HAS mechanisms have been proposed to enhance the Quality of Experience (QoE). However, existing mechanisms ignore the environmental impact in the QoE evaluation of mobile users, while the popularity of mobile video allows users to watch videos in diversified scenarios. In this paper, we propose an environment-aware HAS scheme that fully concentrates on the different criteria for evaluating video QoE under different environments. Using the advantage of the sensors in mobile phones, the scheme constructs and validates a video QoE model based on environment perception and then designs a model-driven, environment-aware HAS rate adaptation algorithm. We also evaluate the scheme with an environment-aware DASH (Dynamic Adaptive Streaming over HTTP) player in real mobile environments. Compared to the benchmark HAS mechanism, the experimental results demonstrate that our scheme can provide appropriate differentiated rate adaptation for different environments, resulting in a higher QoE.

show abstract

Impact of video resolution changes on QoE for adaptive video streaming

Cited by 20 publications

References 15 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

Enhancing QoE for Mobile Users by Environment-Aware HTTP Adaptive Streaming

Contact Info

Product

Resources

About