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Data-Driven QoE Model for Addis Ababa LTE Video Streaming Using Fuzzy Logic Inference System

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dc.contributor.advisor Yihenew, Wondie (PhD)
dc.contributor.author Aysheshim, Demilie
dc.date.accessioned 2020-03-09T05:25:42Z
dc.date.available 2020-03-09T05:25:42Z
dc.date.issued 2020-02-21
dc.identifier.uri http://etd.aau.edu.et/handle/123456789/21035
dc.description.abstract Nowadays, the video streaming services become the most dominant service as people are more interesting watching online television programs and Video on Demand (VoD). This requires a high speed and high capacity network infrastructure. The Long Term Evolution (LTE) network infrastructure of Addis Ababa has faced this network capacity and data rate demand in order to have a good Quality of Experience (QoE). To takeover this challenge, Ethio Telecom should have appropriate assessment methodology for QoE. This thesis outlines the means of QoE modelling issue to measure QoE objectively. It proposes a QoE model which is used to measure the QoE from Quality of Service (QoS) parameters using Fuzzy Logic Inference System (FIS). This has been done by collecting end-to-end QoS data from network management system which is used as input for the model and the proposed model has been validated using a dataset collected from customer survey. The Model developed is essential for replacing the subjective measurement techniques which are costly and inefficient being influential to user context, terminal characteristics, application software, user capability, etc. In addition, the model developed is helpful for business decision making, network planning, optimization and operational support activities in manual systems or system based for self-organizing networks according to the infrastructures implemented. The result of correlation, regression, and Four-way ANOVA show that the Stall Frequency (SF) and the Start Delay (SD) plays the major impact on the LTE video streaming QoE by 33% and 25% respectively. The validation result shows the proposed model is an accurate, consistent and linear compared to the existing models. en_US
dc.language.iso en_US en_US
dc.publisher Addis Ababa University en_US
dc.subject LTE en_US
dc.subject QoE en_US
dc.subject QoS en_US
dc.subject KQI en_US
dc.subject MOS en_US
dc.subject FIS en_US
dc.subject Video Streaming en_US
dc.subject Data-Driven en_US
dc.subject Model en_US
dc.title Data-Driven QoE Model for Addis Ababa LTE Video Streaming Using Fuzzy Logic Inference System en_US
dc.type Thesis en_US

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