A machine learning resource allocation solution to improve video quality in remote education
Article
Comsa, I., Molnar, A., Tal, I., Bergamin, P., Muntean, G., Muntean, C. and Trestian, R. 2021. A machine learning resource allocation solution to improve video quality in remote education. IEEE Transactions on Broadcasting. 67 (3), pp. 664-684. https://doi.org/10.1109/TBC.2021.3068872
Type | Article |
---|---|
Title | A machine learning resource allocation solution to improve video quality in remote education |
Authors | Comsa, I., Molnar, A., Tal, I., Bergamin, P., Muntean, G., Muntean, C. and Trestian, R. |
Abstract | 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. |
Keywords | Streaming media; Video recording; Quality assessment; Education; Internet; Pandemics; Media; Video quality; machine learning; resource allocation; quality of service; technology enhanced learning |
Publisher | IEEE |
Journal | IEEE Transactions on Broadcasting |
ISSN | 0018-9316 |
Electronic | 1557-9611 |
Publication dates | |
Online | 02 Apr 2021 |
03 Sep 2021 | |
Publication process dates | |
Deposited | 08 Apr 2021 |
Accepted | 26 Feb 2021 |
Submitted | 09 Dec 2020 |
Output status | Published |
Accepted author manuscript | |
Copyright 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 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TBC.2021.3068872 |
Web of Science identifier | WOS:000692580200013 |
Language | English |
https://repository.mdx.ac.uk/item/894xy
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