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.
Highlights
Mobile and wireless communication networks have grown to be among the most noteworthy modern achievements, revolutionizing the way people communicate and share information, and facilitating improvements in every aspect of everyday life, including education, media and entertainment, entrepreneurship, transportation, healthcare, security and emergency services, while positively contributing to the economic and social growth both for the developed and developing countries
quality of experience (QoE) has received a lot of research interest in the last years, and has been acknowledged as an important factor in determining network operating efficiency
The first stage in optimizing a mobile multimedia streaming service delivery, is evaluating and predicting the end-user's QoE, which helps in acquiring a better understanding of how the technical aspects of a network impact multimedia service quality as experienced by end-users
Summary
Mobile and wireless communication networks have grown to be among the most noteworthy modern achievements, revolutionizing the way people communicate and share information, and facilitating improvements in every aspect of everyday life, including education, media and entertainment, entrepreneurship, transportation, healthcare, security and emergency services, while positively contributing to the economic and social growth both for the developed and developing countries. Aside from the challenges posed on QoE by the mobility and the necessity of attaining seamless session continuity and seamless horizontal and vertical handover [10], the unremitting emergence of new and complex mobile multimedia services, such as 3D video streaming [11, 12], video gaming, ultrahigh definition (UHD) video, augmented reality (AR), virtual reality (VR) and mixed reality (MR), introduces additional complexity to the QoE provisioning procedure [13] Limitations deriving from both terminal equipment capabilities and transmission channel characteristics, have as well a clear influence on the QoE perception of the end-user within the context of wireless communication systems [14]. The main contributions of this survey can be summarized in the following: 1) according to the best of the authors’ knowledge, this is the first endeavor to present a complete hands-on guide on multimedia services QoE assessment that unlike existing surveys, includes besides conventional video streaming, extended reality and video gaming applications; and 2) up to this date, this is the first survey to provide a comparative examination of MLbased QoE prediction models that focus in particular on extended reality and video gaming applications
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