A supervised machine learning approach for DASH video QoE prediction in 5G networks
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Ul Mustafa, Raza
Rothenberg, Christian E.
Quinlan, Jason J.
Association for Computing Machinery
Future fifth generation (5G) networks are envisioned to provide improved Quality-of-Experience (QoE) for applications by means of higher data rates, low and ultra-reliable latency and very high reliability. Proving increasing beneficial for mobile devices running multimedia applications. However, there exist two main co-related challenges in multimedia delivery in 5G. Namely, balancing operator provisioning and client expectations. To this end, we investigate how to build a QoE-aware network that guarantees at run-time that the end-to-end user experience meets the end users’ expectations at the same that the network’s Quality of Service (QoS) varies. The contribution of this paper is twofold: First, we consider a Dynamic Adaptive Streaming over HTTP (DASH) video application in a realistic emulation environment derived from real 5G traces in static and mobility scenarios to assess the QoE performance of three state-of-art Adaptive Bitrate Streaming (ABS) algorithm categories: Hybrid - Elastic and Arbiter+; buffer-based - BBA and Logistic; and rate-based - Exponential and Conventional. Second, we propose a Machine Learning (ML) classifier to predict user satisfaction which considers network metrics, such as RTT, throughput, and number of packets. Our proposed model does not rely on knowledge about the application or specific traffic information. We show that our ML classifiers achieves a QoE prediction accuracy of 87.63 % and 79 % for static and mobility scenarios, respectively.
5G , QoE prediction , QoS , Machine learning , Video streaming , DASH
Ul Mustafa, R., Ferlin, S., Rothenberg, C. E., Raca, D. and Quinlan, J. J.. (2020) ‘A supervised Machine Learning approach for DASH video QoE prediction in 5G networks’, 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks (Q2SWinet’20), Online [Alicante, Spain], 16-20 Nov. doi: 10.1145/3416013.3426458
© Association for Computing Machinery 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record will be published in ACM Conference Proceedings http://dx.doi.org/10.1145/3416013.3426458