Optimizing Adaptive Video Streaming: A Comprehensive Review of Dynamic Swarm Optimization Models for Network Condition Prediction

This review paper explores the integration of dynamic swarm optimization models into adaptive video streaming systems for enhanced performance in dynamically changing network conditions. As the demand for high-quality streaming experiences continues to rise, the challenges posed by variable network conditions necessitate sophisticated solutions. We delve into the fundamentals of dynamic swarm optimization, discussing its adaptability to evolving environments and its application in predicting network condition changes. The paper examines the practical implementation strategies of these models within adaptive streaming algorithms, detailing their impact on video quality, buffering rates, and overall user satisfaction. Through a comprehensive evaluation of performance metrics and comparisons with traditional methods, we showcase the effectiveness of dynamic swarm optimization. The review concludes by identifying future research directions and addressing existing challenges, advocating for continued exploration of this promising avenue for optimizing adaptive video streaming in dynamic network environments