Title
Analysis and Estimation of Video QoE in Wireless Cellular Networks using Machine Learning
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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value) regarding the same parameters using regression.
Year
DOI
Venue
2019
10.1109/QoMEX.2019.8743281
2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX)
Keywords
Field
DocType
QoE,QoS,Video,MOS,PSNR,LTE
Computer science,Quality of service,Encryption,Mean opinion score,Artificial intelligence,Quality of experience,Bitstream,Radio Link Protocol,Random forest,Video quality,Machine learning
Conference
ISSN
ISBN
Citations 
2372-7179
978-1-5386-8213-5
0
PageRank 
References 
Authors
0.34
15
4
Name
Order
Citations
PageRank
Dimitar Minovski121.37
Christer Åhlund221527.85
Karan Mitra316917.84
Per Johansson400.34