Factors Influencing Quality of Experience

Reiter, Ulrich; Brunnström, Kjell; Moor, Katrien De; Larabi, Mohamed–Chaker; Pereira, Manuela; Pinheiro, António; You, Junyong; Žgank, Andrej

doi:10.1007/978-3-319-02681-7_4

Cited by 157 publications

(150 citation statements)

References 57 publications

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“…For instance, Reiter et al [21]have shown that age, sex and socio-economic status are all factors influencing QoE, while Kara et al [22] claim that economic context such as the brand perception of viewing device (in their case a smartphone) and the price, if any, paid to view the content were significant factors. However, such factors are also difficult to manage and correlate in a unified model for efficient, real-time systems.…”

Section: ) Subjective Qoe Assessment and 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.

148

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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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Section: ) Subjective Qoe Assessment and 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.

148

View full text Add to dashboard Cite

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“…Futhermore, network-centric monitoring does not consider the multi-dimensional aspects of QoE. Beyond the network attributes, these aspects include the application, the system, the context and the user behavior itself [36]. This consideration is particularly highlighted in [39], where QoE is represented as a multi-layered vector.…”

Section: Qos/qoe Monitoring Systemsmentioning

confidence: 99%

“…Those probes will then be monitored centrally. Such an approach may have benefits in terms of end-to-end QoE delivery because it provides ISPs with information related to context-, system-and user-influencing factors (including the geographical location) [36], which relates to the principles of context-aware QoE monitoring. Centralized monitoring systems can utilize cloud technologies to reduce network-wide equipment costs and resolve programmability and flexibility issues.…”

Section: Cloud Processing -Issues Of Scalability and Securitymentioning

confidence: 99%

Challenges of future multimedia QoE monitoring for internet service providers

Robitza

Ahmad

Kara

et al. 2017

Multimed Tools Appl

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The ever-increasing network traffic and user expectations at reduced cost make the delivery of high Quality of Experience (QoE) for multimedia services more vital than ever in the eyes of Internet Service Providers (ISPs Appl (2017) 76:22243-22266 focus on the user, has become essential as the first step in cost-effective provisioning of high quality services. With the recent changes in the perception of user privacy, the rising level of application-layer encryption and the introduction and deployment of virtualized networks, QoE monitoring solutions need to be adapted to the fast changing Internet landscape. In this contribution, we provide an overview of state-of-the-art quality monitoring models and probing technologies, and highlight the major challenges ISPs have to face when they want to ensure high service quality for their customers.

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“…[10], [11]). Three key factors have been shown to influence these dependent variables, namely: human factors, the characteristics of the individual; system factors, the characteristics of the media, device and network used; and contextual factors, additional characteristics such as time, task, and local conditions [12].…”

Section: Related Workmentioning

confidence: 99%

The CP-QAE-I: A video dataset for exploring the effect of personality and culture on perceived quality and affect in multimedia

Guntuku

Scott

Yang

et al. 2015

2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX)

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Abstract-Perception of quality and affect are subjective, driven by a complex interplay between system and human factors. Is it, however, possible to model these factors to predict subjective perception? To pursue this question, broader collaboration is needed to sample all aspects of personality, culture, and other human factors. Thus, an appropriate dataset is needed to integrate such efforts. Here, the CP-QAE-I is proposed. This is a video dataset containing 144 video sequences based on 12 short movie clips. These vary by: frame rate; frame dimension; bit-rate; and affect. An evaluation by 76 participants drawn from the United Kingdom, Singapore, India, and China suggests adequate distinction between the video sequences in terms of perceived quality as well as positive and negative affect. Nationality also emerged as a significant predictor, supporting the rationale for further study. By sharing the dataset, this paper aims to promote work modeling human factors in multimedia perception.

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Factors Influencing Quality of Experience

Cited by 157 publications

References 57 publications

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

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

Challenges of future multimedia QoE monitoring for internet service providers

The CP-QAE-I: A video dataset for exploring the effect of personality and culture on perceived quality and affect in multimedia

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