Qoe Model For Social Media Video Streaming Service Using Ensemble Method - The Case Of Addis Ababa

Nowadays, the number of Social Media (SM) users is increasing tremendously worldwide.rnAn increase in the number of smartphone users and an increase in Internetrncoverage helps people expand their networks. The availability of SM helps users tornfind their friends, make a connections with new people with different skills, and improverntheir careers. The diversity of services on SM also attracts many new users tornuse SM in their day-to-day activities.rnTo deliver SM services, many SM application developers continuously work to improverntheir services and also to add new services to attract new customers and also to keeprntheir users.rnThere are many stakeholders that are involved in end-to-end service delivery of SM.rnThese include Telecom network providers, SM application owners, and end-user devicesrnperformance. Having a good network Quality of Service (QoS) may not guaranteerngood service quality on the customers’ side. The quality of a given service perceivedrnby an end-user, which is Quality of Experience (QoE) is a broad term and influencedrnby many Influencing Factors (IF).rnThere are many research papers done on the QoE model of different SMs. Most of thernpapers focus on the impact of network and application related QoS on the overall customerrnsatisfaction level. Even though these papers incorporate different parameters asrninput features for the QoE model, to the best of the author’s investigation, researchesrnwhich are done in the context of Ethiopia didn’t consider the users device parametersrninfluence on the customers’ satisfaction level.rnThe over all QoE of a service is influenced by many factors, the main focus of thisrnthesis is to provide a QoE model for SM video streaming services by taking different IFrnas input parameters. From the network QoS parameter download speed, upload speed,rnlatency, and jitter are used as inputs. From users’ device parameters phone Random Access Memory (RAM) size, phone’s free internal storage size, and phone’s screenrnresolution are taken as device IF. And from application parameters, video resolutionrnis used as an input for the model.rnThe developed model is based on an ensemble technique which is a Machine Learningrn(ML) based approach. The model has good accuracy, which is 94.1% accuracy. Inrnaddition to the accuracy, based on the importance of each input feature to the finalrnmodel, download speed takes the main influencing share by 52.357% from the totalrninput parameters and from the users’ mobile device parameter free internal storagernspace has 10.784% and mobile RAM size 9.4% on the final QoE model.rnGenerally, this work meets the initial objective by developing QoE model with a goodrnaccuracy, and shows the influence of other parameter other than the usual networkrnQoS parameters and gives insight to the gaps that cause the customers’ dissatisfactionrnof the SM video services by considering different IF.

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