Haewon Byeon, Desidi Narsimha Reddy, Divya Nimma, J. Balajee, G. Siva Nageswara Rao, Aseel Smerat, Mukesh Soni
With the advent of 6G and edge intelligence, medical emergency systems require ultra-reliable, low-latency, and adaptive communication to support real-time diagnosis and collaborative treatment. This study proposes a 6G-enabled edge-enhanced functional chain scheduling framework for intelligent medical emergency assistance in smart healthcare networks. Medical services are classified and prioritized into four categories based on urgency and service region, and corresponding priority weights are assigned. A next-generation health information network architecture integrating 6G communication, edge computing, Software Defined Networking (SDN), and Network Function Virtualization (NFV) is designed to ensure dynamic and context-aware resource orchestration. A service function chain (SFC) scheduling model is formulated with the objective of minimizing the total weighted completion time, representing the latency–priority tradeoff across diverse healthcare demands. To optimize scheduling, a matching game algorithm is developed for small-scale edge scenarios, while a Q-learning reinforcement learning algorithm is designed for large-scale distributed networks. Simulation experiments demonstrate that the proposed hybrid model effectively balances computational load and network latency, ensuring prioritized service delivery for critical medical emergencies. This research provides a scalable and intelligent foundation for 6G edge-assisted healthcare communication, enabling seamless collaboration among hospitals, ambulances, and remote medical units.