Northampton Electronic Collection of Theses and Research

Quality of Experience Experimentation Prediction Framework through Programmable Network Management

Al-Mashhadani, A. O. B., Mu, M. and Al-Sharbaz, A. (2022) Quality of Experience Experimentation Prediction Framework through Programmable Network Management. Network. 2(4), pp. 500-518. 2673-8732.

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Al_Mashadani_etal_Network_2022_Quality_of_Experience_Experimentation_Prediction_Framework_through_Programmable_Network_Management
Al_Mashadani_etal_Network_2022_Quality_of_Experience_Experimentation_Prediction_Framework_through_Programmable_Network_Management.pdf - Published Version
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Abstract:
Quality of experience (QoE) metrics can be used to assess user perception and satisfaction in data services applications delivered over the Internet. End-to-end metrics are formed because QoE is dependent on both the users’ perception and the service used. Traditionally, network optimization has focused on improving network properties such as the quality of service (QoS). In this paper we examine adaptive streaming over a software-defined network environment. We aimed to evaluate and study the media streams, aspects affecting the stream, and the network. This was undertaken to eventually reach a stage of analysing the network’s features and their direct relationship with the perceived QoE. We then use machine learning to build a prediction model based on subjective user experiments. This will help to eliminate future physical experiments and automate the process of predicting QoE.
Uncontrolled Keywords:
QoE, fairness, SDN, classification prediction, DASH, multimedia
Creators:
Al-Mashhadani, A. O. B., Mu, M. and Al-Sharbaz, A.
Date:
8 October 2022
Date Type:
Publication
Page Range:
pp. 500-518
Journal or Publication Title:
Network
Volume:
2
Number:
4
Number of Pages:
19
Language:
English
DOI:
10.3390/network2040030
ISSN:
2673-8732
Status:
Published / Disseminated
Refereed:
Yes
Related URLs:
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