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-highdefinition 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.

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The paper presents a short overview of the evolution of the Radio Access Network (RAN) towards virtualized, open and intelligent RAN architectures. Major KPIs used for performance analysis in the process of the evolution of the RAN are identified. KPIs that are considered of primary importance for ensuring high-quality multimedia communications (MMC) in a scenario of wireless Radio Access Networks based on 4G/5G Open-RAN architectures are measured and analyzed. Different split scenarios according to the O-RAN specifications are considered, as well as relevant to MMC KPIs, such as round trip time, delay jitter and packet loss. Although the variation in the results from the evaluation of these parameters in different RAN architecture scenarios is not drastic, in the context of MMC and future near to-real time services, the results show advantages of these scenarios over legacy RAN architectures. In addition, we propose a network slicing model for optimizing the network performance and enhancing the quality of MMC, by implementing optimal utilization of network resources.