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.

Real-time transmission of large-scale data, high computational demands, and resource limitations on edge devices pose significant challenges for intelligent sports systems. The proliferation of Internet of Things (IoT) technologies has catalyzed the rise of Smart Sport, where wearable sensors, cameras, and intelligent algorithms are integrated to revolutionize athletic training. Despite this transformation, access to professional coaching remains constrained by factors such as time, cost, and scalability. A critical limitation of existing remote coaching approaches is their inability to perform effective spatio-temporal analysis, hindering comprehensive evaluation and refinement of athletic performance. This paper introduces MRCoach, a mixed reality-based, immersive, and interactive sports coaching system that enables data-driven training without requiring in-person supervision. MRCoach reconstructs 3D volumetric avatars of both learners and expert athletes, allowing users to visualize and compare their movements side-by-side in a mixed reality environment for intuitive skill refinement. To ensure responsive and efficient feedback, we propose an adaptive semantic transmission strategy that prioritizes sport-relevant joints, thereby reducing latency and bandwidth requirements without sacrificing accuracy. Furthermore, a 3D sports analysis framework is developed to evaluate motion based on normalized joint positions, velocities, and accelerations. This framework computes real-time similarity scores and delivers actionable guidance to learners. Experimental results across four sports—tennis, soccer, basketball, and baseball—demonstrate MRCoach’s effectiveness in providing personalized, real-time training experiences. Compared to state-of-the-art baselines such as MagicStream, ExPose, and PIXIE, MRCoach achieves significantly lower end-to-end latency (79.9 ms) and higher frame rates (≥ 56 FPS), while maintaining accurate pose tracking and high avatar fidelity.

In medical procedures such as surgery and diagnosis, it is crucial to provide doctors and nurses with up-to-date patient information. In this paper, we propose Health-MR, a portable Mixed-Reality (MR) system that helps medical staff monitor patient conditions. Health-MR consists of three components: (1) Patient Identification Recognition using face detection, (2) Medical Cloud Database for patient information retrieval, and (3) Non-invasive Heart Rate Measurement via image processing and Fast Fourier Transform (FFT). Our evaluation demonstrates that Health-MR significantly reduces the time needed to query patient information and provides remote, accurate, and real-time heart rate monitoring.