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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: 2020-03-17Bibliographically approved
In thesis
1. Towards Quality of Experience for Industrial Internet of Things
Open this publication in new window or tab >>Towards Quality of Experience for Industrial Internet of Things
2020 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [sv]

Today’s research on Quality of Experience (QoE) mainly addresses multimedia services, where the end-users' subjective perception is the prime factor of determining the QoE. With the introduction of the Internet of Things (IoT), there is a need for new ways of evaluating the QoE. Emerging IoT services, such as remotely-controlled operations, autonomous vehicles (AVs), and energy management, are more complex, creating additional quality requirements emerging from the machine-to-machine (M2M) communication and autonomous processes. One challenge, as an extension of the legacy QoE concept, is understanding the perception of end-users QoE in the context of IoT services. For instance, within the current state of the art in QoE it is not clear how intelligent machines can impact end-users' QoE, but also how end-users can alter or affect an intelligent machine. Another challenge is the quality evaluation of the M2M and systems that enable the machines to run by themselves. Consider a self-driving vehicle, where multiple autonomous decisions are simultaneously made as a result of predictive models that reason on the vehicle's generated data. An evaluation of the predictive models is inevitable due to abundance of the potential sources of failures. A quality degradation of the IoT hardware, the software enabling autonomous decision, and the M2M communication can raise life-threatening concerns, directly impacting the end-users' QoE. In this thesis, we argue for a paradigm shift in the QoE area that understands the relationships between humans and intelligent machines, as well as within the machines. Our contributions are as follows: first, we introduce the term Quality of IoT-experience (QoIoT) to extend the conventional QoE approaches in covering IoT services. Within QoIoT, we consider a quality evaluation from the perspective of the end-users, as well as from intelligent machines. The end-user's perception is captured by following the conventional QoE approaches, while regarding intelligent machine we propose the usage of objective metrics to describe their experiences and performance. As our second contribution, we propose a novel QoIoT architecture that consists of a layered methodology in order to determine the overall QoIoT. The QoIoT architecture, firstly, models the data-sources of an IoT service, classified within four layers: physical, network, application, and virtual. Secondly, the architecture proposes three layers for measuring the QoIoT by considering Quality of Data (QoD), Quality of Network (QoN), and Quality of Context (QoC), with QoC being the prime layer in measuring the objective performance metrics. Finally, the third contribution considers a case-study of cellular IoT, involving autonomous mining vehicles, which we utilize to achieve a preliminary results that validate the proposed QoIoT architecture.

Place, publisher, year, edition, pages
Sweden: Luleå University of Technology, 2020
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
QoE, IoT, AI, ML
National Category
Computer Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-78084 (URN)978-91-7790-558-5 (ISBN)978-91-7790-559-2 (ISBN)
Presentation
2020-04-21, Room A, Forskargatan 1, Skellefteå, 10:00 (English)
Opponent
Supervisors
Available from: 2020-03-17 Created: 2020-03-17 Last updated: 2020-03-31Bibliographically approved

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Minovski, DimitarÅhlund, ChristerMitra, Karan

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