Quality of Experience Experimentation Prediction Framework through Programmable Network Management

Abstract

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.

Keywords

• QoE
• fairness
• SDN
• classification prediction
• DASH
• multimedia