Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future Frontiers

Abstract

This review paper explores the paradigm of personalized adaptive streaming, where machine learning techniques are employed to tailor video streaming experiences based on individual user behavior, preferences, and contextual factors. The paper begins by elucidating the evolution of video streaming and the critical role of adaptive streaming in modern multimedia consumption. It provides a comprehensive overview of adaptive video streaming, covering its basics, traditional approaches, and associated challenges. Emphasizing the significance of personalization in enhancing user experience, the paper then delves into the integration of machine learning in adaptive streaming systems. Specific personalized adaptive streaming techniques, including user profiling, context-aware adaptation, and real-time adjustments based on user behavior, are discussed in detail. Case studies and applications showcase notable platforms, successes, and challenges. A comparative analysis of machine learning models and algorithms is conducted, followed by a discussion on current challenges, ethical considerations and future research directions. The paper concludes by summarizing key findings and urging researchers and industry practitioners to contribute to the evolving landscape of personalized adaptive streaming.