QoE-Oriented Intelligent Resource Scheduling in Immersive XR Services over 5G Network Slices
Keywords:
Quality of Experience; Immersive Extended Reality; 5G network slicing; intelligent resource scheduling; deep reinforcement learning; edge computing; multi-tenancy; fairnessAbstract
The convergence of fifth-generation mobile networks and immersive extended reality applications introduces stringent demands for ultra-reliable, low-latency, and high-throughput connectivity, all of which must be satisfied within highly dynamic and multi-tenant network environments. Network slicing has emerged as a foundational enabler of differentiated service provisioning, yet conventional slice management schemes are predominantly oriented toward network-centric quality-of-service metrics and therefore fail to capture the perceptual, context-sensitive dimensions of human quality of experience. This paper presents a system-level examination of quality-of-experience-oriented intelligent resource scheduling for immersive extended reality services delivered over 5G network slices. The study is structured around a holistic architectural framework that integrates perceptual QoE modeling, slice-aware orchestration, closed-loop intelligent decision-making using deep reinforcement learning, and multi-tenant governance mechanisms. The analysis emphasizes structural trade-offs among granularity of resource control, control loop latency, fairness across vertical service providers, energy sustainability, and operational resilience. By examining the interplay among these dimensions, the paper highlights how QoE-centric metrics can be embedded into slice resource schedulers without sacrificing network efficiency or violating service-level agreements. Deployment considerations are discussed across edge-cloud continuums, and the paper further addresses policy, regulatory, and socio-technical implications, including algorithmic transparency, data privacy, and accountability in autonomous resource management. The discussion is cast in continuous academic prose that synthesizes insights from architectural systems research, network softwarization, machine learning for resource allocation, and communication policy studies. The result is a comprehensive forward-looking perspective that positions QoE-aware slice scheduling as a critical integration point where technical innovation, governance frameworks, and human-centric design principles must co-evolve.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
