Best prediction of perceived user quality in mobile networks
Supervisor: Dr John Schormans
Research group(s): Networks
Enhancing the traditional network QoS metrics of packet loss, delay and jitter, mobile network operators increasingly rely on a new metric called “QoE” – Quality of Experience. QoE provides an objective measure of the end user’s satisfaction level. For example, Huawei has recently introduced U-vMOS as a user experience grading system for videos, (http://www.huawei.com/en/news/2016/4/U-vMOS-Supports-for-Video-Communication). As well as international organisations like Huawei, many smaller companies have recently started up in this area. These new companies offer specialist QoE measurement services, e.g. https://www.thousandeyes.com/ and https://www.actual-experience.com/. QoE is often predicted through measurement of the QoS metrics of packet loss, delay and jitter. However, currently there are no studies of how accurately and precisely QoS sampling enables the prediction of QoE. Preliminary work at QMUL has shown that, unless operators can account for the errors in precision and accuracy, they may predict GOOD QoE when the QoE is POOR, and POOR when it is actually GOOD. Because of its growing importance such errors in QoE prediction can now have huge commercial implications. The aim of this research project is to discover the fundamental networking relationships that affect QoE prediction, and ultimately to guide network operators in making the best use of their QoS measurements so that they can optimally predict end user QoE.