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research-article

Predicting quality of experience in multimedia streaming

Authors:

Vlado Menkovski,

Adetola Oredope,

Antonio Liotta,

Antonio Cuadra SánchezAuthors Info & Claims

MoMM '09: Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia

Pages 52 - 59

https://doi.org/10.1145/1821748.1821766

Published: 14 December 2009 Publication History

Abstract

Measuring and predicting the user's Quality of Experience (QoE) of a multimedia stream is the first step towards improving and optimizing the provision of mobile streaming services. This enables us to better understand how Quality of Service (QoS) parameters affect service quality, as it is actually perceived by the end user. Over the last years this goal has been pursued by means of subjective tests and through the analysis of the user's feedback. Existing statistical techniques have lead to poor accuracy (order of 70%) and inability to evolve prediction models with the system's dynamics. In this paper, we propose a novel approach for building accurate and adaptive QoE prediction models using Machine Learning classification algorithms, trained on subjective test data. These models can be used for real-time prediction of QoE and can be efficiently integrated into online learning systems that can adapt the models according to changes in the environment. Providing high accuracy of above 90%, the classification algorithms become an indispensible component of a mobile multimedia QoE management system.

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Cited By

Kougioumtzidis GPoulkov VZaharis ZLazaridis P(2022)A Survey on Multimedia Services QoE Assessment and Machine Learning-Based PredictionIEEE Access10.1109/ACCESS.2022.314959210(19507-19538)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3149592
Chemodanov DCalyam PValluripally STrinh HPatman JPalaniappan K(2021)On QoE-Oriented Cloud Service Orchestration for Application ProvidersIEEE Transactions on Services Computing10.1109/TSC.2018.286685114:4(1194-1208)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TSC.2018.2866851
Karadža ABaraković Husić JBaraković SNogo S(2020)Multidimensional QoE Prediction of WebRTC Video Communication with Machine LearningAdvanced Technologies, Systems, and Applications V10.1007/978-3-030-54765-3_18(269-283)Online publication date: 5-Nov-2020
https://doi.org/10.1007/978-3-030-54765-3_18
Show More Cited By

Index Terms

Predicting quality of experience in multimedia streaming
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Inductive logic learning
  2. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
2. Networks
  1. Network services
    1. Network management

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Information

Published In

cover image ACM Other conferences

MoMM '09: Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia

December 2009

663 pages

ISBN:9781605586595

DOI:10.1145/1821748

General Chair:
Gabriele Kotsis
Johannes Kepler University Linz, Austria
,
Program Chairs:
David Taniar
Monash University, Australia
,
Eric Pardede
La Trobe University, Australia

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Johannes Kepler University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 December 2009

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MoMM '09

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MoMM '09: 7th International Conference on Mobile Computing and Multimedia

December 14 - 16, 2009

Kuala Lumpur, Malaysia

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Cited By

Kougioumtzidis GPoulkov VZaharis ZLazaridis P(2022)A Survey on Multimedia Services QoE Assessment and Machine Learning-Based PredictionIEEE Access10.1109/ACCESS.2022.314959210(19507-19538)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3149592
Chemodanov DCalyam PValluripally STrinh HPatman JPalaniappan K(2021)On QoE-Oriented Cloud Service Orchestration for Application ProvidersIEEE Transactions on Services Computing10.1109/TSC.2018.286685114:4(1194-1208)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TSC.2018.2866851
Karadža ABaraković Husić JBaraković SNogo S(2020)Multidimensional QoE Prediction of WebRTC Video Communication with Machine LearningAdvanced Technologies, Systems, and Applications V10.1007/978-3-030-54765-3_18(269-283)Online publication date: 5-Nov-2020
https://doi.org/10.1007/978-3-030-54765-3_18
Tetarave STripathy SGhosh R(2020)Enhancing quality of experience using peer‐to‐peer overlay on device‐to‐device communicationsInternational Journal of Communication Systems10.1002/dac.454633:15Online publication date: 28-Jul-2020
https://doi.org/10.1002/dac.4546
Goran NBegovic ASkaljo N(2019)Comparing simulation model for objective QoE video evaluation with real IPTV test scenario during appearance of packet losses2019 27th Telecommunications Forum (TELFOR)10.1109/TELFOR48224.2019.8971318(1-4)Online publication date: Nov-2019
https://doi.org/10.1109/TELFOR48224.2019.8971318
Zheng XJin YShi XSong YZhao X(2019)UCWE: A User-Centric Approach for Web Quality of Experience Measurement2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00135(928-935)Online publication date: Dec-2019
https://doi.org/10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00135
Mozo ASegall IMargolin UGomez-Canaval S(2019)Scalable Prediction of Service-Level Events in Datacenter Infrastructure Using Deep Neural NetworksIEEE Access10.1109/ACCESS.2019.29561827(179779-179798)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2956182
Barman NJammeh EGhorashi SMartini M(2019)No-Reference Video Quality Estimation Based on Machine Learning for Passive Gaming Video Streaming ApplicationsIEEE Access10.1109/ACCESS.2019.29204777(74511-74527)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2920477
Wei XZhou LWei XZhou L(2019)Technical PremiseMultimedia QoE Evaluation10.1007/978-3-030-23350-1_2(7-20)Online publication date: 6-Aug-2019
https://doi.org/10.1007/978-3-030-23350-1_2
Ben Letaifa A(2019)WBQoEMS: Web browsing QoE monitoring system based on prediction algorithmsInternational Journal of Communication Systems10.1002/dac.400732:13Online publication date: 20-Jun-2019
https://doi.org/10.1002/dac.4007
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