QoE Model for Social Media Video Streaming Service Using Ensemble Method - The Case of Addis Ababa
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Date
2022-01
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Addis Ababa University
Abstract
Nowadays, the number of Social Media (SM) users is increasing tremendously worldwide.
An increase in the number of smartphone users and an increase in Internet
coverage helps people expand their networks. The availability of SM helps users to
find their friends, make a connections with new people with different skills, and improve
their careers. The diversity of services on SM also attracts many new users to
use SM in their day-to-day activities.
To deliver SM services, many SM application developers continuously work to improve
their services and also to add new services to attract new customers and also to keep
their users.
There are many stakeholders that are involved in end-to-end service delivery of SM.
These include Telecom network providers, SM application owners, and end-user devices
performance. Having a good network Quality of Service (QoS) may not guarantee
good service quality on the customers’ side. The quality of a given service perceived
by an end-user, which is Quality of Experience (QoE) is a broad term and influenced
by many Influencing Factors (IF).
There are many research papers done on the QoE model of different SMs. Most of the
papers focus on the impact of network and application related QoS on the overall customer
satisfaction level. Even though these papers incorporate different parameters as
input features for the QoE model, to the best of the author’s investigation, researches
which are done in the context of Ethiopia didn’t consider the users device parameters
influence on the customers’ satisfaction level.
The over all QoE of a service is influenced by many factors, the main focus of this
thesis is to provide a QoE model for SM video streaming services by taking different IF
as input parameters. From the network QoS parameter download speed, upload speed,
latency, 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 screen
resolution are taken as device IF. And from application parameters, video resolution
is used as an input for the model.
The developed model is based on an ensemble technique which is a Machine Learning
(ML) based approach. The model has good accuracy, which is 94.1% accuracy. In
addition to the accuracy, based on the importance of each input feature to the final
model, download speed takes the main influencing share by 52.357% from the total
input parameters and from the users’ mobile device parameter free internal storage
space has 10.784% and mobile RAM size 9.4% on the final QoE model.
Generally, this work meets the initial objective by developing QoE model with a good
accuracy, and shows the influence of other parameter other than the usual network
QoS parameters and gives insight to the gaps that cause the customers’ dissatisfaction
of the SM video services by considering different IF.
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Keywords
QoE, QoS, Social Media, Ensemble Method, Machine Learning