Dynamic Bitrate Adaptation Models in Adaptive Video Streaming: A Comprehensive Review and Comparative Analysis

As digital video consumption continues to surge, the demand for adaptive video streaming technologies that can seamlessly adjust to varying network conditions, buffer sizes, and viewer preferences becomes increasingly critical. This paper presents a comprehensive review and comparative analysis of dynamic bitrate adaptation models within the realm of adaptive video streaming. We delve into the fundamentals of adaptive video streaming, exploring the intricacies of dynamic bitrate adaptation and the associated challenges. Emphasis is placed on dissecting the factors influencing bitrate adaptation, namely network conditions, buffer size dynamics, and viewer preferences. The core of this paper revolves around an indepth exploration of various mathematical models employed for dynamic bitrate adaptation. We categorize these models based on their underlying algorithms, scrutinizing their strengths and weaknesses. A comparative analysis is conducted to evaluate their performance under diverse real-world scenarios, considering the unpredictability of network conditions, resource constraints, and adaptability to different content types. The paper further presents case studies and practical implementations that illustrate the application of dynamic bitrate adaptation models, discussing successes, challenges, and lessons learned. Finally, the review concludes with an overview of current trends and future directions in dynamic bitrate adaptation for adaptive video streaming. By synthesizing existing knowledge and identifying potential avenues for future research, this paper aims to provide a valuable resource for researchers, developers, and practitioners working in the evolving landscape of adaptive video streaming technologies.