ImmersiveSlicing: An O-RAN Cross-Layer Reinforcement Learning Framework for Low-Latency Immersive Applications

The proliferation of immersive applications such as Virtual, Augmented, and Mixed Reality (VR/AR/MR) imposes stringent low-latency and reliability requirements that challenge conventional O-RAN slicing mechanisms. Existing frameworks often fail to anticipate rapid XR traffic fluctuations driven by user motion and gaze dynamics, leading to inefficient resource utilization and SLA violations. To overcome these limitations, we propose a cross-layer intelligent control framework that integrates traffic prediction and reinforcement learning-based slice orchestration across the Non-RT and Near-RT RIC. By coupling long-term foresight with short-term adaptability, the proposed design enables proactive, SLA-aware scheduling under highly dynamic conditions. We further develop a trace-driven network emulator to reproduce realistic 5G behaviors and validate system robustness. Extensive experiments demonstrate that our framework consistently achieves over 95% SLA compliance, below 2% latency violations, and up to 30% latency reduction compared with state-of-the-art baselines, confirming its effectiveness and scalability for next-generation immersive networks.