Improved quality of online education using prioritized multi-agent reinforcement learning for video traffic scheduling
Article
Comsa, I., Molnar, A., Tal, I., Imhof, C., Bergamin, P., Muntean, G., Muntean, C. and Trestian, R. 2023. Improved quality of online education using prioritized multi-agent reinforcement learning for video traffic scheduling. IEEE Transactions on Broadcasting. 69 (2), pp. 436-454. https://doi.org/10.1109/TBC.2023.3246815
Type | Article |
---|---|
Title | Improved quality of online education using prioritized multi-agent reinforcement learning for video traffic scheduling |
Authors | Comsa, I., Molnar, A., Tal, I., Imhof, C., Bergamin, P., Muntean, G., Muntean, C. and Trestian, R. |
Abstract | The recent global pandemic has transformed the way education is delivered, increasing the importance of videobased online learning. However, this puts a significant pressure on the underlying communication networks and the limited available bandwidth needs to be intelligently allocated to support a much higher transmission load, including video-based services. In this context, this paper proposes a Machine Learning (ML)-based solution that dynamically prioritizes content viewers with heterogeneous video services to increase their Quality of Service (QoS) and perceived Quality of Experience (QoE). The proposed approach makes use of the novel Prioritized Multi- Agent Reinforcement Learning solution (PriMARL) to decide the prioritization order of the video-based services based on networking conditions. However, the performance in terms of QoS and QoE provisioning to learners with different profiles and networking conditions depends on the type of scheduler employed in the frequency domain to conduct the scheduling and the radio resource allocation. To decide the best approach to be followed, we employ the proposed PriMARL solution with different types of scheduling rules and compare them with other state-of-theart solutions in terms of throughput, delay, packet loss, Peak Signal-to-Noise Ratio (PSNR), and Mean Opinion Score (MOS) for different traffic loads and characteristics. We show that the proposed solution achieves the best user QoE results. |
Keywords | Quality of experience; Quality of service; Streaming media; Pandemics; Education; Resource management; Reinforcement learning; Machine learning; multi-agent reinforcement learning; video traffic prioritization; QoE; online education |
Sustainable Development Goals | 4 Quality education |
Middlesex University Theme | Sustainability |
Publisher | IEEE |
Journal | IEEE Transactions on Broadcasting |
ISSN | 0018-9316 |
Electronic | 1557-9611 |
Publication dates | |
Online | 16 Mar 2023 |
07 Jun 2023 | |
Publication process dates | |
Deposited | 15 Feb 2023 |
Accepted | 09 Feb 2023 |
Submitted | 09 Nov 2022 |
Output status | Published |
Publisher's version | License |
Copyright Statement | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0 |
Web address (URL) | https://ieeexplore.ieee.org/document/10073590 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TBC.2023.3246815 |
Web of Science identifier | WOS:000953757600001 |
Language | English |
https://repository.mdx.ac.uk/item/8q469
Download files
Publisher's version
76
total views18
total downloads2
views this month2
downloads this month