Real-time Adaptation with Machine Learning in Adaptive Video Streaming: Challenges, Opportunities, and Future Directions

— As the demand for high-quality video content delivery continues to rise, the importance of adaptive video streaming has become paramount. Real-time adaptation, facilitated by machine learning algorithms, stands at the forefront of enhancing user experiences by dynamically adjusting video quality based on network conditions and device capabilities. This review paper comprehensively explores the challenges and opportunities associated with implementing real-time machine learning algorithms in adaptive video streaming. We delve into the intricacies of latency, computational requirements, and scalability, addressing the evolving landscape of video streaming protocols. The paper surveys the use of machine learning models for bitrate adaptation and examines their role in minimizing latency while considering computational efficiency. Additionally, we explore strategies for optimizing computational requirements and scalability in real-time machine learning systems. Through a thorough analysis of case studies and implementations, we showcase practical applications and lessons learned from deploying real-time adaptive streaming solutions. Finally, we present future directions and opportunities for further research, shedding light on the evolving intersection of real-time adaptation and machine learning in the realm of adaptive video streaming. This review aims to provide a comprehensive understanding of the current state of the field and inspire future advancements in this rapidly evolving domain