Prediction of perceptual quality for mobile video using fuzzy inference systems

Alreshoodi, Mohammed; Danish, Emad; Woods, John; Fernando, Anil; Alwis, Chamitha de

doi:10.1109/tce.2015.7389811

Cited by 17 publications

(9 citation statements)

References 33 publications

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“…QoS to QoE mapping, by exploring and establishing a relationship between QoS metrics and QoE for specific use cases is a primary way to achieve such objective metrics. For instance, the H.264/AVC-encoded 3D video model proposed by Alreshoodi et al [23], maps QoS parameters from both the video encoding layer (content type, spatial resolution and quantization parameter) and the network layer (packet loss rate and mean burst length). Their model was developed using fuzzy logic inference systems, and may have significant system complexity and computational power requirements.…”

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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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

View full text Add to dashboard Cite

show abstract

“…Furthermore, Callet et al, 2012;and Möller, (2010) define QoE as the degree of delight or annoyance of the user of an application or service, in the context of communication services it is influenced by content, network, device, application, user expectations and goal, and context of use. Studies reported by Alreshoodi et al (2015); Rivera et al (2013) focused on the effects of network impairments on video streaming QoE. The authors indicated the significant impact of network impairment on video streaming QoE.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Influence of Smart-device Visual Features, Viewing Distance, and Content Factors on Video Streaming QoE

Mongi

2022

TJET

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Quality of experience (QoE) over wireless networks has attracted attention from industry and academia due to an increase in video streaming applications. Several researchers have attempted to understand the factors affecting QoE and design appropriate quality control strategies. Normally, video streaming is initiated by a user who accesses video contents over wireless networks using a smart device held at various viewing distances. Each aforementioned factor has the potential to affect QoE of the viewed session. However, several studies explore the behavior of wireless networks on video streaming QoE. To understand the effects of other factors on QoE, this paper investigates the influence of the device's visual features, viewing distance, and content factors on video streaming. The study adopted an emulation technique to conduct multi-factor experiments designed using the Taguchi method. The 5-ways ANOVA analysis revealed that the effects of smart-device visual features, viewing distance, and content types are significant on video streaming QoE at p<0.05. Moreover, smart devices with a pixel density index of more than 200 ppi produce high QoE, with the viewing distance limited to 45 cm. Lastly, the video bitrate greater than 1024 kbps produced a good QoE regardless of the frame rates.

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“…Many of the works in this eld are constructed based on QoS parameters and subjective QoE obtained from o ine testing of a limited number of users in a laboratory environment and on a speci c service. The services covered include video-based service [7][8][9][10][11][12][13][14][15][16][17], Audiovisual [18], multimedia service [5], [19] and VoIP [20][21].…”

Section: Introductionmentioning

confidence: 99%

Service Level Agreement Optimization via Reverse Engineering the QoE/QoS Relations

Mousavi

Kazemitabar

Ebrahimzadeh

2022

Preprint

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Along with the growth of the Internet comes a competitive environment among Internet service providers. In this regard, quality of service (QoS) and customer's quality of experience (QoE) is introduced as the two main criteria of satisfaction for network users/regulators. In this paper, we have evaluated these two criteria for one of the largest Internet service communication networks in Iran. By providing a predictive model, we propose solutions to improve the quality of the communication network. Because the quality of experience can only be achieved through direct contact with users - a time-consuming and costly process - the use of a predictive model can be very helpful. Our model predicts the quality of experience from the quality-of-service parameters with an accuracy of about ninety percent. Next, we reverse-engineer the relationship between the quality of experience and quality of service to develop a service level agreement (SLA) contract. The relationship between quality of experience and quality of service is then compiled into a set of if-then rules. In this regard, using a decision tree classifier, we were able to set the quality-of-service parameter thresholds for the gold, silver, and bronze SLAs.

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Prediction of perceptual quality for mobile video using fuzzy inference systems

Cited by 17 publications

References 33 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

Analyzing the Influence of Smart-device Visual Features, Viewing Distance, and Content Factors on Video Streaming QoE

Service Level Agreement Optimization via Reverse Engineering the QoE/QoS Relations

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