AI-Driven Network Quality of Service and Quality of Experience Optimization: A Review of Intelligent Resource Management
Abstract
Ensuring high Quality of Service (QoS) and Quality of Experience (QoE) has become increasingly challenging as networks support heterogeneous applications ranging from real-time communications and video streaming to immersive extended-reality services and machine-type communication. Conventional optimization techniques often struggle to simultaneously consider application requirements, network conditions, user behavior, and resource constraints. Artificial Intelligence (AI) provides mechanisms for learning complex relationships between network performance and user-perceived service quality. This review investigates AI-based approaches for QoS and QoE optimization across wired, wireless, edge, 5G, and 6G networks. Machine learning, deep learning, reinforcement learning, and hybrid optimization methods are examined for applications including bandwidth allocation, latency management, adaptive video streaming, routing, scheduling, and service prioritization. Existing approaches are compared according to optimization objectives, input features, learning methodologies, datasets, and evaluation metrics. Challenges related to subjective QoE measurement, real-time adaptation, fairness, data scarcity, and computational complexity are analyzed. Future research directions involving multimodal AI, federated learning, digital twins, and generative AI are discussed.
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