QoE Prediction Model and its Application in Video Quality Adaptation Over UMTS Networks

Khan, Asiya; Sun, Lingfen; Ifeachor, Emmanuel

doi:10.1109/tmm.2011.2176324

Cited by 160 publications

(110 citation statements)

References 34 publications

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“…User studies: Prior work by the multimedia community try to assess video quality by performing subjective user studies and validating objective video quality models against the user study scores [11,18,25,30,32]. User studies are typically done at a small-scale with a few hundred users and the perceptual scores given by users under a controlled setting may not translate into measures of user engagement in the wild.…”

Section: Related Workmentioning

confidence: 99%

Developing a predictive model of quality of experience for internet video

BalachandranAthula

SekarVyas

AkellaAditya

et al. 2013

SIGCOMM Comput. Commun. Rev.

184

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Improving users' quality of experience (QoE) is crucial for sustaining the advertisement and subscription based revenue models that enable the growth of Internet video. Despite the rich literature on video and QoE measurement, our understanding of Internet video QoE is limited because of the shift from traditional methods of measuring video quality (e.g., Peak Signal-to-Noise Ratio) and user experience (e.g., opinion scores). These have been replaced by new quality metrics (e.g., rate of buffering, bitrate) and new engagement-centric measures of user experience (e.g., viewing time and number of visits). The goal of this paper is to develop a predictive model of Internet video QoE. To this end, we identify two key requirements for the QoE model: (1) it has to be tied in to observable user engagement and (2) it should be actionable to guide practical system design decisions. Achieving this goal is challenging because the quality metrics are interdependent, they have complex and counter-intuitive relationships to engagement measures, and there are many external factors that confound the relationship between quality and engagement (e.g., type of video, user connectivity). To address these challenges, we present a data-driven approach to model the metric interdependencies and their complex relationships to engagement, and propose a systematic framework to identify and account for the confounding factors. We show that a delivery infrastructure that uses our proposed model to choose CDN and bitrates can achieve more than 20% improvement in overall user engagement compared to strawman approaches.

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Section: Related Workmentioning

confidence: 99%

Developing a predictive model of quality of experience for internet video

BalachandranAthula

SekarVyas

AkellaAditya

et al. 2013

SIGCOMM Comput. Commun. Rev.

184

View full text Add to dashboard Cite

show abstract

“…To evaluate our proposed approach of QoE IFs classification, we conducted two evaluations using six QoE estimation methods based on [2] [13] [8], shown in Table 1. The first one is a non discriminant evaluation (considering all QoE IFs) and the second one is a discriminant evaluation to highlight the fact that categories do not affect in the same the user perception.…”

Section: Qoe Estimation Used Methodsmentioning

confidence: 99%

A Hierarchical Classification Model of QoE Influence Factors

Amour

Souihi

Hoceini

et al. 2015

Lecture Notes in Computer Science

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Abstract. Quality of Service (QoS) optimization are not sufficient to ensure users needs. That's why, operators are investigating a new concept called Quality of Experience (QoE), to evaluate the real quality perceived by users. This concept becomes more and more important, but still hard to estimate. This estimation can be influenced by a lot of factors called : Quality of Experience Influence Factors (QoE IFs). In this work, we survey and review existing approaches to classify QoE IFs. Then, we present a new modular and extensible classification architecture. Finally, regarding the proposed classification, we evaluate some QoE estimation approaches to highlight the fact that categories do not affect in the same the user perception.

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“…Another QoS/QoE function was proposed by Hsu and Lo [25] for cloud-based multicast video streaming using a simulated platform. Finally, Khan [26] et al designed a QoE metric for H.264 video in 3G networks simulated by the ns2 simulator.…”

Section: ) Objective Qoe Modelsmentioning

confidence: 99%

5G-QoE: QoE Modelling for Ultra-HD Video Streaming in 5G Networks

Nightingale

Salvá-García

Calero

et al. 2018

IEEE Trans. on Broadcast.

153

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This version is available at https://strathprints.strath.ac.uk/65207/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. IEEE Transactions on BroadcastingPaper Identification Number BTS-yr-xxx (DOUBLE-CLICK HERE TO EDIT) 1  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 modelling 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 applications 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.Index Terms-QoE, 5G networks, video streaming, UHD.

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QoE Prediction Model and its Application in Video Quality Adaptation Over UMTS Networks

Cited by 160 publications

References 34 publications

Developing a predictive model of quality of experience for internet video

Developing a predictive model of quality of experience for internet video

A Hierarchical Classification Model of QoE Influence Factors

5G-QoE: QoE Modelling for Ultra-HD Video Streaming in 5G Networks

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