Machine Learning Based QoE Estimation Model for Video Streaming over UMTS Network

The advent of data-intensive services needs quality Internet services. This in turn, makes Quality
of Experience (QoE) gain prominent recognition in the telecommunications industry. Ethio telecom
uses network Quality of Service (QoS) monitoring data obtained from Network Management
Systems (NMS) tools to comprehend its network performances. However, as QoS measurement
refers to network performances, this method does not generally give QoE data as perceived
by the user. Therefore, QoE estimation models are proposed as solutions in the literature,
recently.
This study focuses on developing QoE estimation models using QoS features of round-trip time
(RTT), jitter, loss rate (LR) and throughput, and QoE scores collected using Application for prediCting
QUality of experience at Interne Access (ACQUA)-based crowdsourcing in Universal
Mobile Telecommunication Systems (UMTS) networks in a real-time basis. Data preparations
techniques such as data cleaning and dataset imbalance corrections have been applied to the
collected datasets. Machine Learning (ML) algorithms of Articial Neural Network (ANN), KNearest
Neighbor (KNN) and Random Forest (RF) are selected based on their suitability for multilabel
problems. After training these models developed, they are evaluated using commonly used
performance metrics such as accuracy, Root Mean Square Error (RMSE) and Receiver Operating
Characteristics (ROC).