[2311.07399] Context-Aware Adaptive Prefetching for DASH Streaming over 5G Networks
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Computer Science > Networking and Internet Architecture

arXiv:2311.07399 (cs)
[Submitted on 13 Nov 2023]

Title:Context-Aware Adaptive Prefetching for DASH Streaming over 5G Networks

Authors:Juncal Uriol, Inhar Yeregui, Alvaro Gabilondo, Roberto Viola, Pablo Angueira, Jon Montalban
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Abstract:The increasing consumption of video streams and the demand for higher-quality content drive the evolution of telecommunication networks and the development of new network accelerators to boost media delivery while optimizing network usage. Multi-access Edge Computing (MEC) enables the possibility to enforce media delivery by deploying caching instances at the network edge, close to the Radio Access Network (RAN). Thus, the content can be prefetched and served from the MEC host, reducing network traffic and increasing the Quality of Service (QoS) and the Quality of Experience (QoE). This paper proposes a novel mechanism to prefetch Dynamic Adaptive Streaming over HTTP (DASH) streams at the MEC, employing a Machine Learning (ML) classification model to select the media segments to prefetch. The model is trained with media session metrics to improve the forecasts with application layer information. The proposal is tested with Mobile Network Operators (MNOs)' 5G MEC and RAN and compared with other strategies by assessing cache and player's performance metrics.
Subjects: Networking and Internet Architecture (cs.NI); Multimedia (cs.MM)
Cite as: arXiv:2311.07399 [cs.NI]
  (or arXiv:2311.07399v1 [cs.NI] for this version)
  https://doi.org/10.48550/arXiv.2311.07399
Journal reference: 2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2023, pp. 1-6
Related DOI: https://doi.org/10.1109/BMSB58369.2023.10211275

Submission history

From: Roberto Viola [view email]
[v1] Mon, 13 Nov 2023 15:27:23 UTC (1,586 KB)
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