Multilayer perceptron neural network-based QoS-aware, content-aware and device-Aware QoE prediction model: a proposed prediction model for medical ultrasound streaming over small cell networks

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Rehman, Ikram ORCID: https://orcid.org/0000-0003-0115-9024, Nasralla, Moustafa and Philip, Nada (2019) Multilayer perceptron neural network-based QoS-aware, content-aware and device-Aware QoE prediction model: a proposed prediction model for medical ultrasound streaming over small cell networks. Electronics, 8 (2). ISSN 2079-9292

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Official URL: https://doi.org/10.3390/electronics8020194

Abstract

This paper presents a QoS-aware, content-aware and device-aware non-intrusive medical QoE (m-QoE) prediction model over small cell networks. The proposed prediction model utilises a Multilayer Perceptron (MLP) neural network to predict m-QoE. It also acts as a platform to maintain and optimise the acceptable diagnostic quality through a device-aware adaptive video streaming mechanism. The proposed model is trained for an unseen dataset of input variables such as QoS, content features, and display device characteristics, to produce an output value in the form of m-QoE (i.e. MOS). The efficiency of the proposed model is validated through subjective tests carried by medical experts. The prediction accuracy obtained via the correlation coefficient and Root Mean-Square-Error (RMSE) indicates that the proposed model succeeds in measuring m-QoE closer to the visual perception of the medical experts. Furthermore, we have addressed the following two main research questions: (1) How significant is ultrasound video content type in determining m-QoE? and (2) How much of a role does the screen size and device resolution play in medical experts’ diagnostic experience? The former is answered through the content classification of ultrasound video sequences based on their spatio-temporal features, by including these features in the proposed prediction model, and validating their significance through medical experts’ subjective ratings. The latter is answered by conducting a novel subjective experiment of the ultrasound video sequences across multiple devices.

Item Type:	Article
Identifier:	10.3390/electronics8020194
Keywords:	Mobile health (M-health); Small cell networks; MLP Neural networks; Medical Quality of Service (m-QoS); Medical Quality of Experience (m-QoE)
Subjects:	Computing
Depositing User:	Ikram Rehman
Date Deposited:	10 Jul 2019 08:05
Last Modified:	04 Nov 2024 11:53
URI:	https://repository.uwl.ac.uk/id/eprint/6225

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