Personalized Video Streaming: A Comprehensive Review of User-Centric Mathematical Models for Adaptive Content Delivery

This review paper delves into the evolving landscape of personalized video streaming, focusing on the integration of usercentric mathematical models into adaptive content delivery systems. Traditional approaches to adaptive streaming, while effective, often neglect individual user preferences, viewing habits, and contextual factors. In response to this limitation, a growing body of research has emerged, exploring mathematical models that prioritize the personalization of streaming experiences. This paper provides a comprehensive examination of these user-centric models, discussing their application in optimizing adaptive streaming algorithms. We explore mathematical frameworks that consider individual preferences, viewing habits, and context, shedding light on the intricate interplay between these factors and streaming quality. The review includes an in-depth analysis of optimization techniques employed in these models, a discussion of challenges faced, and proposed solutions. Additionally, real-world case studies showcase the practical implementation and effectiveness of user-centric mathematical models in enhancing the overall streaming experience. The paper concludes by outlining future directions for research in this dynamic field, emphasizing the significance of personalized streaming in the future of adaptive video delivery. Through this exploration, we aim to contribute to the ongoing discourse on the convergence of mathematical modeling and user-centricity in the domain of adaptive video streaming