QoE Modelling, Measurement and Prediction: A Review

TL;DR

This review paper surveys current research on modeling, measuring, and predicting user experience quality in mobile networks, highlighting strengths, weaknesses, and future directions for personalized service improvements.

Contribution

It provides a comprehensive overview of existing QoE techniques, analyzing their effectiveness and identifying gaps for future research in mobile computing environments.

Findings

Existing QoE models vary in accuracy and applicability.

Current measurement techniques face challenges in real-time prediction.

Future research should focus on personalized and adaptive QoE prediction methods.

Abstract

In mobile computing systems, users can access network services anywhere and anytime using mobile devices such as tablets and smart phones. These devices connect to the Internet via network or telecommunications operators. Users usually have some expectations about the services provided to them by different operators. Users' expectations along with additional factors such as cognitive and behavioural states, cost, and network quality of service (QoS) may determine their quality of experience (QoE). If users are not satisfied with their QoE, they may switch to different providers or may stop using a particular application or service. Thus, QoE measurement and prediction techniques may benefit users in availing personalized services from service providers. On the other hand, it can help service providers to achieve lower user-operator switchover. This paper presents a review of the state-the-art research in the area of QoE modelling, measurement and prediction. In particular, we investigate and discuss the strengths and shortcomings of existing techniques. Finally, we present future research directions for developing novel QoE measurement and prediction techniques