Analysis and Estimation of Video QoE in Wireless Cellular Networks using Machine Learning

Document identifier: oai:DiVA.org:ltu-76619
Access full text here:10.1109/QoMEX.2019.8743281
Keyword: Natural Sciences, Computer and Information Sciences, Media and Communication Technology, Naturvetenskap, Data- och informationsvetenskap, Medieteknik, QoE, QoS, Video, MOS, PSNR, LTE, Pervasive Mobile Computing, Distribuerade datorsystem
Publication year: 2019
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructure
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

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.

Authors

Dimitar Minovski

Luleå tekniska universitet; Datavetenskap
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Christer Åhlund

Luleå tekniska universitet; Datavetenskap
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Karan Mitra

Luleå tekniska universitet; Datavetenskap
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Per Johansson

InfoVista Sweden AB
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