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Supervised-learning-Based QoE Prediction of Video Streaming in Future Networks: A Tutorial with Comparative Study

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journal contribution
posted on 2023-10-12, 11:02 authored by Arslan AhmadArslan Ahmad, Atif Bin Mansoor, Alcardo Alex BarakabitzeAlcardo Alex Barakabitze, andrew hines, Luigi Atzori, Ray Walshe

 Quality of experience (QoE)-based service management remains key for successful provisioning of multimedia services in next-generation networks such as 5G/6G, which requires proper tools for quality monitoring, prediction, and resource management where machine learning (ML) can play a crucial role. In this article, we provide a tutorial on the development and deployment of the QoE measurement and prediction solutions for video streaming services based on supervised learning ML models. First, we provide a detailed pipeline for developing and deploying super-vised-learning-based video streaming QoE prediction models that covers several stages including data collection, feature engineering, model optimization and training, testing and prediction, and evaluation. Second, we discuss the deployment of the ML model for QoE prediction/measurement in 5G/6G networks using network-enabling technologies such as software-defined networking, network function virtualization, and multi-access edge computing by proposing reference architecture. Third, we present a comparative study of the state-of-the-art supervised learning ML models for QoE prediction of video streaming applications based on multiple performance metrics. 

Funding

Global Academies Fellowship Cardiff Metropolitan University

History

Publisher

IEEE

Version

  • AM (Accepted Manuscript)

Citation

Ahmad, A., Mansoor, A.B., Barakabitze, A.A., Hines, A., Atzori, L. and Walshe, R. (2021) 'Supervised-learning-based QoE prediction of video streaming in future networks: A tutorial with comparative study', IEEE Communications Magazine, 59(11), pp.88-94. doi: 10.1109/MCOM.001.2100109

Print ISSN

0163-6804

Electronic ISSN

1558-1896

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Arslan Ahmad

Copyright Holder

  • © The Publisher

Publisher Rights Statement

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Language

  • en

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