Research Repository

A hybrid prediction model for video quality by QoS/QoE mapping in wireless streaming

E. Danish, and A. Fernando, and M. Alreshoodi, and J. Woods, (2015) A hybrid prediction model for video quality by QoS/QoE mapping in wireless streaming. In: 2015 IEEE International Conference on Communication Workshop (ICCW), 2015-06-08 - 2015-06-12.

Full text not available from this repository.

Abstract

In the video streaming arena, and especially within the wireless transmission domain, measuring users' quality of experience (QoE) has become a pressing issue for it offers several benefits to both the service provider and the end user. However, available measurement techniques that adopt a full reference model (FR) are impractical in real-time transmission scenarios since they require the original video sequence available at the receiver's end. Hence, no-reference (NR) models fill this gap by providing less accurate measurement but sufficiently reliable for real-time video streaming. In this paper, we propose and evaluate a hybrid no-reference prediction model for the perceptual quality of video in the wireless domain. The model is based on fuzzy inference systems (FIS), and exploits several key parameters from both the application layer and physical layer. Hence the model is realized by means of mapping quality of service to quality of experience (QoS/QoE). The model is evaluated in contrast to random neural networks (RNN), and simulation results show considerable prediction accuracy of the model with a correlation coefficient of 92.17% and 0.1098 root mean square error.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Notes: In the video streaming arena, and especially within the wireless transmission domain, measuring users' quality of experience (QoE) has become a pressing issue for it offers several benefits to both the service provider and the end user. However, available measurement techniques that adopt a full reference model (FR) are impractical in real-time transmission scenarios since they require the original video sequence available at the receiver's end. Hence, no-reference (NR) models fill this gap by providing less accurate measurement but sufficiently reliable for real-time video streaming. In this paper, we propose and evaluate a hybrid no-reference prediction model for the perceptual quality of video in the wireless domain. The model is based on fuzzy inference systems (FIS), and exploits several key parameters from both the application layer and physical layer. Hence the model is realized by means of mapping quality of service to quality of experience (QoS/QoE). The model is evaluated in contrast to random neural networks (RNN), and simulation results show considerable prediction accuracy of the model with a correlation coefficient of 92.17% and 0.1098 root mean square error.
Uncontrolled Keywords: correlation theory; fuzzy neural nets; fuzzy reasoning; image sequences; least mean squares methods; prediction theory; quality of experience; quality of service; random processes; video communication; video streaming; FIS; FR model; NR model; QoE mapping; QoS mapping; RNN; application layer; correlation coefficient; end user; full reference model; fuzzy inference systems; hybrid no-reference prediction model; physical layer; random neural networks; root mean square error; service provider; video perceptual quality; video sequence; wireless domain; wireless transmission domain; wireless video streaming arena; Packet loss; Predictive models; Quality assessment; Solid modeling; Streaming media; Video recording; Estimation; Fuzzy; H.264; MOS; Perceptual; VQM
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 24 Sep 2016 11:26
Last Modified: 15 Jan 2022 00:58
URI: http://repository.essex.ac.uk/id/eprint/17638

Actions (login required)

View Item View Item