QoE-aware QoS management

Agboma, Florence; Liotta, Antonio

doi:10.1145/1497185.1497210

Cited by 89 publications

(54 citation statements)

References 14 publications

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“…This work extends previous findings reported in [1,2,5] and [3,6], which focused mainly on the collection of QoE data for different types of services. In [5] a QoE management methodology was proposed, which shows how QoE data may be used for the benefit of network operators.…”

supporting

confidence: 87%

“…1, we have previously reported our experimental findings (subjective QoE data 1 ) related to steps 1 to 5 for different applications/services in [1,2,5] and [3,6]. These findings are the outcome of a 3-year research work.…”

Section: Qoe Data Gatheringmentioning

confidence: 63%

“…In [5] a QoE management methodology was proposed, which shows how QoE data may be used for the benefit of network operators. The work herein significantly extends that of [5] by providing a comprehensive and well-analysed study of QoE over a broader range of scenarios and for different types of mobile devices (mobile phone, PDA and Laptop). By means of further examples, we illustrated the applicability of the QoE management strategies for each scenario.…”

mentioning

confidence: 99%

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Quality of experience management in mobile content delivery systems

Agboma

Liotta

2010

Telecommun Syst

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This study contributes towards the relatively new but growing discipline of QoE management in content delivery systems. The study focuses on the development of a QoE-based management framework for the construction of QoE models for different types of multimedia contents delivered onto three typical mobile terminals-a mobile phone, PDA and a laptop. A statistical modelling technique is employed which, correlates QoS parameters with estimates of QoE perceptions. These correlations were found to be dependent on terminals and multimedia content types. The application of the framework and prediction models in QoE management strategies are demonstrated using examples. We find that significant resource savings can be achieved with our approach by contrast to conventional QoS solutions.

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supporting

confidence: 87%

Section: Qoe Data Gatheringmentioning

confidence: 63%

mentioning

confidence: 99%

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Quality of experience management in mobile content delivery systems

Agboma

Liotta

2010

Telecommun Syst

Self Cite

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“…From the results, the accuracy of the DT model (J84)is validated for three terminals and was:93.55% (Mobile phones),90.29% (PDA) and95.46% (Laptop). For the SVM model (SMO) the accuracy was 88.59% (Mobile phones), 89.38% (PDAs) and 91.45% (Laptops).They found from the results that both methods outperform the Discriminate Analysis method which was used in [29]. However, the error of these modelsis between 10% and 20%, as shown in [37].Work in [38] has also used DT and SVM for building an objective QoE model and then compares them with other Machine Learning methods including: Naive Bayes (NB), kNearest Neighbours (k-NN), Random Forest (RF) and Neural Networks (NNet).…”

Section: Qoemodels Based Onmachine Learning Methodsmentioning

confidence: 98%

Survey on Qoe\Qos Correlation Models Formultimedia Services

Alreshoodi¹,

Woods²

2013

IJDPS

100

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“…In this study [135] the subjective evaluation is implemented using the 'Method of Limits' [92]. Thus is used to detect the thresholds by changing a single stimulus in successive, discrete steps.…”

Section: Subjective Qoe Models Developed In the Labmentioning

confidence: 99%

Computational Inference and Control of Quality in Multimedia Services

Menkovski

2015

Springer Theses

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Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:www.tue.nl/taverne Take down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nl providing details and we will investigate your claim. Copyright c 2013 by Vlado Menkovski All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without the prior written consent of the author. Typeset using L A T E X, printed in The NetherlandsTo my parents, Angel who dedicated his life to his sons and Snežana whose strength inspires me deeply. SummaryQuality is the degree of excellence we expect of a service or a product. It is also one of the key factors that determine its value. For multimedia services, understanding the experienced quality means understanding how the delivered fidelity, precision and reliability correspond to the users' expectations. Yet the quality of multimedia services is inextricably linked to the underlying technology. It is developments in video recording, compression and transport as well as display technologies that enables high quality multimedia services to become ubiquitous. The constant evolution of these technologies delivers a steady increase in performance, but also a growing level of complexity. As new technologies stack on top of each other the interactions between them and their components become more intricate and obscure. In this environment optimizing the delivered quality of multimedia services becomes increasingly challenging. The factors that affect the experienced quality, or Quality of Experience (QoE), tend to have complex non-linear relat...

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QoE-aware QoS management

Cited by 89 publications

References 14 publications

Quality of experience management in mobile content delivery systems

Quality of experience management in mobile content delivery systems

Survey on Qoe\Qos Correlation Models Formultimedia Services

Computational Inference and Control of Quality in Multimedia Services

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