An Adaptive Optimized Machine Learning–Driven Congestion Control Model for QoS Enhancement in Bandwidth-Restricted Wireless Networks

Abstract

Modern wireless networks generally function under a *best-effort service model*, which, although capable of handling both real-time and non-real-time traffic, often fails to deliver the required Quality of Service (QoS) for delay-sensitive applications. Real-time services such as video streaming, Voice over IP (VoIP), and online gaming depend heavily on stable network conditions and require low latency to perform effectively. However, since the best-effort approach does not differentiate between traffic types, network performance tends to degrade under heavy load, resulting in problems like high jitter, increased delay, and packet loss.

In wireless networks, QoS is typically evaluated using key performance indicators such as throughput, jitter, delay, and packet loss—metrics that directly influence both network efficiency and user experience. High latency or data loss can severely affect performance, especially for applications that demand consistent, real-time communication.

To address these limitations, this study proposes a novel QoS management framework specifically designed for bandwidth-constrained wireless environments, where optimal use of limited resources is essential. Rather than adopting the conventional method of over-provisioning bandwidth—which can be both wasteful and expensive—the proposed approach integrates differentiated services with dynamic scheduling driven by real-time monitoring of data rates and packet classification.

By continuously adjusting resource allocation in response to changing traffic conditions, the framework ensures that real-time traffic receives the required priority, while non-real-time data is handled efficiently without causing congestion. This adaptive mechanism leads to better utilization of available bandwidth, as resources are assigned dynamically based on actual network demands instead of fixed, static allocations.

The system also introduces an optimized queuing mechanism that prioritizes packets according to their type and the current queue length. This design reduces latency for high-priority (real-time) packets while maintaining fair processing for lower-priority traffic, preventing bottlenecks within the network. As a result, packet delays and losses are minimized, and QoS performance improves significantly—even in scenarios with limited bandwidth.

Additionally, the proposed model reduces unnecessary resource over-provisioning, a common shortcoming in traditional architectures that often leads to underutilization or inflated costs. Overall, this adaptive QoS framework provides a cost-effective, scalable, and efficient solution for managing congestion and enhancing the performance of real-time applications in wireless networks.