Adaptive QoS-Aware Link Scheduling for Scalable and Interference-Constrained Wireless Networks

Strong Quality of Service (QoS) assurance is necessary in contemporary wireless networks due to the fast growth of data-intensive and low-latency applications, such as live video streaming, industrial automation, and IoT-based smart systems. However, obtaining consistent communication performance is severely hampered by interference-limited environments, dynamic topologies, and dense deployments. This paper suggests an Adaptive QoS-Aware Link Scheduling (AQLS) framework for scalable and interference-constrained wireless networks as a solution to these problems. The proposed approach models the network using a conflict graph and formulates the scheduling process as an Integer Linear Programming (ILP) problem that simultaneously considers QoS priorities and interference constraints. To ensure real-time applicability, adaptive heuristic and learning-based approximations are introduced to efficiently approximate near-optimal scheduling decisions. Simulation results using NS-3 and MATLAB demonstrate that AQLS performs significantly better than greedy schemes and traditional TDMA in terms of throughput, end-to-end delay, packet delivery ratio, and fairness. Because it achieves a special balance between optimization accuracy, scalability, and QoS adaptability, the proposed framework is a strong candidate for 5G/6G, automotive, and large-scale IoT communication systems.