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A Supervised Machine Learning Approach for DASH Video QoE Prediction in 5G Networks

Authors:

Raza Ul Mustafa,

Christian Esteve Rothenberg,

Jason J. QuinlanAuthors Info & Claims

Q2SWinet '20: Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks

Pages 57 - 64

https://doi.org/10.1145/3416013.3426458

Published: 16 November 2020 Publication History

Abstract

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 achieve a QoE prediction accuracy of 87.63 % and 79 % for static and mobility scenarios, respectively.

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Cited By

Ghosh MSinghal C(2025)A review on machine learning based user-centric multimedia streaming techniquesComputer Communications10.1016/j.comcom.2024.108011231(108011)Online publication date: Mar-2025
https://doi.org/10.1016/j.comcom.2024.108011
Zinno SBotta AVentre GPasquino N(2024)Prediction of Round-Trip Time in 5G NSA Networks in Urban Environment2024 IEEE International Symposium on Measurements & Networking (M&N)10.1109/MN60932.2024.10615319(1-6)Online publication date: 2-Jul-2024
https://doi.org/10.1109/MN60932.2024.10615319
Kalan RDulger I(2024)A Survey on QoE Management Schemes for HTTP Adaptive Video Streaming: Challenges, Solutions, and OpportunitiesIEEE Access10.1109/ACCESS.2024.349161312(170803-170839)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3491613
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Index Terms

A Supervised Machine Learning Approach for DASH Video QoE Prediction in 5G Networks
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression
2. Networks
  1. Network architectures
    1. Programming interfaces

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Published In

cover image ACM Conferences

Q2SWinet '20: Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks

November 2020

139 pages

ISBN:9781450381208

DOI:10.1145/3416013

General Chair:
Cheng Li
Memorial University of Newfoundland, Canada
,
Program Chair:
Ahmed Mostefaoui
Femto-ST Institute, University of Franche-Comte, France

Copyright © 2020 ACM.

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SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Published: 16 November 2020

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MSWiM '20: 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 16 - 20, 2020

Alicante, Spain

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Overall Acceptance Rate 46 of 131 submissions, 35%

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Cited By

Ghosh MSinghal C(2025)A review on machine learning based user-centric multimedia streaming techniquesComputer Communications10.1016/j.comcom.2024.108011231(108011)Online publication date: Mar-2025
https://doi.org/10.1016/j.comcom.2024.108011
Zinno SBotta AVentre GPasquino N(2024)Prediction of Round-Trip Time in 5G NSA Networks in Urban Environment2024 IEEE International Symposium on Measurements & Networking (M&N)10.1109/MN60932.2024.10615319(1-6)Online publication date: 2-Jul-2024
https://doi.org/10.1109/MN60932.2024.10615319
Kalan RDulger I(2024)A Survey on QoE Management Schemes for HTTP Adaptive Video Streaming: Challenges, Solutions, and OpportunitiesIEEE Access10.1109/ACCESS.2024.349161312(170803-170839)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3491613
Mustafa RIslam MRothenberg CGomes P(2023)EFFECTOR: DASH QoE and QoS Evaluation Framework For EnCrypTed videO tRafficNOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium10.1109/NOMS56928.2023.10154448(1-8)Online publication date: 8-May-2023
https://doi.org/10.1109/NOMS56928.2023.10154448
Zinno SAffinito APasquino NVentre GBotta A(2023)Prediction of RTT Through Radio-Layer Parameters in 4G/5G Dual-Connectivity Mobile Networks2023 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC58397.2023.10218091(213-218)Online publication date: 9-Jul-2023
https://doi.org/10.1109/ISCC58397.2023.10218091
Ajeyprasaath KVetrivelan P(2023)A QoE Framework for Video Services in 5G Networks with Supervised Machine Learning ApproachMachine Learning and Computational Intelligence Techniques for Data Engineering10.1007/978-981-99-0047-3_56(661-668)Online publication date: 16-May-2023
https://doi.org/10.1007/978-981-99-0047-3_56
Ajeyprasaath KVetrivelan PChang EGomathi S(2023)A Survey of QoE Framework for Video Services in 5G NetworksFuturistic Communication and Network Technologies10.1007/978-981-19-8338-2_45(541-551)Online publication date: 19-May-2023
https://doi.org/10.1007/978-981-19-8338-2_45
Apruzzese GVladimirov RTastemirova ALaskov P(2022)Wild Networks: Exposure of 5G Network Infrastructures to Adversarial ExamplesIEEE Transactions on Network and Service Management10.1109/TNSM.2022.318893019:4(5312-5332)Online publication date: Dec-2022
https://doi.org/10.1109/TNSM.2022.3188930
Kougioumtzidis GPoulkov VZaharis ZLazaridis P(2022)A Survey on Multimedia Services QoE Assessment and Machine Learning-Based PredictionIEEE Access10.1109/ACCESS.2022.314959210(19507-19538)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3149592
Qiao WDong PDu XZhang YZhang HGuizani M(2022)QoS provision for vehicle big data by parallel transmission based on heterogeneous network characteristics predictionJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.01.018163:C(83-96)Online publication date: 1-May-2022
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