SwarmStream: A User-Centric Approach to Adaptive Video Streaming Using Swarm Optimization Algorithms

In the rapidly evolving landscape of online video streaming, the demand for personalized and adaptive experiences has become paramount. This paper introduces "SwarmStream," an innovative framework that harnesses the power of swarm optimization algorithms to create user-centric adaptive streaming models. Addressing the shortcomings of traditional adaptive streaming approaches, SwarmStream dynamically adapts to individual preferences, historical viewing patterns, and real-time context-aware adjustments. By integrating swarm intelligence, our model optimizes streaming parameters, ensuring an unparalleled viewing experience tailored to each user. We delve into the fundamentals of swarm optimization algorithms, explore their integration into adaptive streaming models, and discuss the representation of users and content within this context. The paper also examines the modeling of individual preferences, leveraging historical viewing patterns, and implementing context-aware adaptations. Through comprehensive evaluation metrics and case studies, we showcase the effectiveness of SwarmStream in enhancing user satisfaction and engagement. As personalized streaming continues to shape the future of online content consumption, SwarmStream represents a significant step forward, offering a dynamic and adaptive solution that responds to the unique needs of each viewer