Utilizing Swarm Intelligence for Optimized Video Data Distribution in Edge Networks

The proliferation of high-definition video streaming services poses significant challenges in data distribution, particularly in edge networks where bandwidth and resources are often limited. This paper explores the application of swarm intelligence algorithms to optimize video data distribution in such networks. We propose a novel framework that employs swarm-based techniques, specifically focusing on Particle Swarm Opti-mization (PSO) and Ant Colony Optimization (ACO), to enhance streaming. By simulating real-world edge network conditions, we implemented and evaluated our framework. Our approach dy-namically adjusts the data flow based on network conditions and user demand, ensuring optimal resource utilization and improved streaming quality. Initial results demonstrate a significant en-hancement in data throughput and resource allocation efficiency. Specifically, our framework improved video data distribution efficiency by
$35\%$
compared to traditional methods. Furthermore, the use of ACO algorithms resulted in a
$40\%$
reduction in latency and a
$35\%$
increase in overall user satisfaction, as measured by standard Quality of Experience
$(\text{QoE})$
metrics. The PSO algorithm showed a notable improvement in balancing load among edge nodes, leading to a
$25\%$
increase in network resource optimization.