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Analysis and Estimation of Video QoE in Wireless Cellular Networks using Machine Learning
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-1139-6998
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-8681-9572
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-3489-7429
InfoVista Sweden AB.
2019 (English)In: 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The use of video streaming services are increasing in the cellular networks, inferring a need to monitor video quality to meet users' Quality of Experience (QoE). The so-called no-reference (NR) models for estimating video quality metrics mainly rely on packet-header and bitstream information. However, there are situations where the availability of such information is limited due to tighten security and encryption, which necessitates exploration of alternative parameters for conducting video QoE assessment. In this study we collect real-live in-smartphone measurements describing the radio link of the LTE connection while streaming reference videos in uplink. The radio measurements include metrics such as RSSI, RSRP, RSRQ, and CINR. We then use these radio metrics to train a Random Forrest machine learning model against calculated video quality metrics from the reference videos. The aim is to estimate the Mean Opinion Score (MOS), PSNR, Frame delay, Frame skips, and Blurriness. Our result show 94% classification accuracy, and 85% model accuracy (R 2 value) when predicting the MOS using regression. Correspondingly, we achieve 89%, 84%, 85%, and 82% classification accuracy when predicting PSNR, Frame delay, Frame Skips, and Blurriness respectively. Further, we achieve 81%, 77%, 79%, and 75% model accuracy (R 2 value) regarding the same parameters using regression.

Place, publisher, year, edition, pages
IEEE, 2019.
Keywords [en]
QoE, QoS, Video, MOS, PSNR, LTE
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing; Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-76619DOI: 10.1109/QoMEX.2019.8743281ISBN: 978-1-5386-8212-8 (electronic)OAI: oai:DiVA.org:ltu-76619DiVA, id: diva2:1368059
Conference
2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), 5-7 June 2019,Berlin, Germany
Available from: 2019-11-05 Created: 2019-11-05 Last updated: 2019-11-05Bibliographically approved

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Minovski, DimitarÅhlund, ChristerMitra, Karan

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