Swarm Intelligence-Based Quality of Experience Optimization in Adaptive Video Streaming: A Comprehensive Review and Future Directions

With the ever-growing demand for high-quality video streaming, adaptive video streaming systems face significant challenges in ensuring a satisfactory user experience. This review paper explores the potential of Swarm Intelligence (SI) algorithms, such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), in optimizing multiple factors influencing Quality of Experience (QoE) in adaptive video streaming. We delve into the intricacies of video quality, buffering, and startup delay, identifying their interdependencies and impact on user satisfaction. The paper provides a detailed examination of SI algorithms, discussing their principles and advantages. Through specific examples and case studies, we showcase how SI can effectively enhance video quality, reduce buffering, and minimize startup delays. Comparative analyses with traditional optimization methods highlight the superior performance of SI in the context of QoE. The review also addresses challenges and open issues, paving the way for future research directions. We conclude with a call to action for continued exploration of SI-based QoE optimization in adaptive video streaming, emphasizing its significance in meeting the evolving demands of multimedia content delivery