article · Computer Networks and Communications
The integration of Quality of Experience Management (QoEM) into mobile networks has significantly transformed the telecommunications industry by aligning service delivery more closely with user expectations. This paradigm shift is particularly crucial as user perception of service quality (i.e., perceived QoE) has become a key differentiator in competitive telecom markets, directly impacting overall user satisfaction and retention. This paper presents a comprehensive review of current QoEM techniques, with a particular emphasis on Machine Learning (ML) approaches for predicting user experience and ensuring high-quality service delivery. Additionally, the integration of Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) is highlighted as a key trend in the development of user centric management systems, enabling dynamic network adjustments based on real-time QoE feedback. However, despite these advancements, the transition from traditional Quality of Service (QoS) metrics to QoE-aware frameworks presents significant challenges. These include the complexity of balancing resource allocation across diverse services to maintain optimal user experiences, as well as technical constraints in real-time QoE monitoring. Furthermore, as demand for high-definition streaming and low-latency applications continues to grow, advanced traffic management solutions are becoming increasingly essential. This review also explores emerging trends in Value-Added Service(s) (VAS), particularly within the context of 5G and 6G networks. We conclude this paper by indicating that the effective advancement of QoEM in mobile networks requires interdisciplinary collaboration between academia and industry. Key areas of focus include network architecture, user behaviour analytics, and content delivery mechanisms. A multidisciplinary approach is essential for addressing the complexities of existing QoEM models and ensuring superior user experiences in next-generation networks.
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DOI: 10.37256/cnc.3220256766
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