A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

Georgios Kougioumtzidis, Vladimir Poulkov, Zaharias Zaharis, Pavlos Lazaridis

Research output: Contribution to journalArticlepeer-review

Abstract

The groundbreaking evolution in mobile and wireless communication networks design in recent years, in combination with the advancement of mobile terminal equipment capabilities, has led in an exponential growth of mobile internet technologies, and arose an ever-growing demand for innovative multimedia services. The highly demanding in terms of network resources over-the-top media services, as well as the emergence of new and complex mobile multimedia services such as video gaming, ultra-high-definition video, and extended reality, requires the enhancement of end-users' perceived quality of experience (QoE). QoE has garnered much research interest in recent years, and has emerged as a key component in the evaluation of network services and operations. As a result, a QoE-aware network planning approach is getting increasingly favored, and novel design challenges, such as how to quantify and measure QoE, have arisen. In this regard, a paradigm shift in network implementations is being envisioned, in which the focus will be on machine learning (ML) methodologies for developing QoE prediction models, directly related to end-user's personalized experience. In this survey, an analysis on application-oriented, ML-based QoE prediction models for the goal of QoE management for multimedia services is presented. In addition, an examination of the state-of-the-art ML-based QoE predictive models and some of the innovative techniques and challenges related to multimedia services quality assessment with focus on extended reality and video gaming applications are outlined.

Original languageEnglish
Article number9706206
Pages (from-to)19507-19538
Number of pages32
JournalIEEE Access
Volume10
Early online date7 Feb 2022
DOIs
Publication statusPublished - 24 Feb 2022

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