Quality of Experience Experimentation Prediction Framework through Programmable Network Management

Al-Mashhadani, Ahmed Osama Basil, Mu, Mu and Al-Sherbaz, Ali ORCID: 0000-0002-0995-1262 (2022) Quality of Experience Experimentation Prediction Framework through Programmable Network Management. Network, 2 (4). pp. 500-518. doi:10.3390/network2040030

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11877Al-Mashhadani, Mu and Al-Sherbaz (2022) Quality_of_Experience_Experimentation_Prediction_Framework_through_Programmable_Network_Management.pdf - Published Version
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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.

Item Type: Article
Article Type: Article
Uncontrolled Keywords: QoE; fairness; SDN; classification prediction; DASH; multimedia
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas: Applied Business & Technology
Depositing User: Kate Greenaway
Date Deposited: 23 Nov 2022 15:59
Last Modified: 31 Oct 2023 12:36
URI: https://eprints.glos.ac.uk/id/eprint/11877

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