A machine learning resource allocation solution to improve video quality in remote education

Comsa, Ioan-Sorin ORCID logoORCID: https://orcid.org/0000-0002-9121-0286, Molnar, Andreea, Tal, Irina, Bergamin, Per, Muntean, Gabriel-Miro, Muntean, Cristina Hava and Trestian, Ramona ORCID logoORCID: https://orcid.org/0000-0003-3315-3081 (2021) A machine learning resource allocation solution to improve video quality in remote education. IEEE Transactions on Broadcasting, 67 (3) . pp. 664-684. ISSN 0018-9316 [Article] (doi:10.1109/TBC.2021.3068872)

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The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pressure on the underlying network infrastructure. This affects negatively content delivery and consequently user perceived quality, especially for video-based services. Focusing on this problem, this paper proposes a machine learning-based resource allocation solution that improves the quality of video services for increased number of viewers. The solution is deployed and tested in an educational context, demonstrating its benefit in terms of major quality of service parameters for various video content, in comparison with existing state of the art. Moreover, a discussion on how the technology is helping to mitigate the effects of massively increasing internet traffic on the video quality in an educational context is also presented.

Item Type: Article
Keywords (uncontrolled): Video quality, machine learning, resource allocation, quality of service, technology enhanced learning
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 32759
Notes on copyright: © 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
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Depositing User: Ramona Trestian
Date Deposited: 08 Apr 2021 11:21
Last Modified: 29 Nov 2022 17:43
URI: https://eprints.mdx.ac.uk/id/eprint/32759

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