Optimizing Adaptive Video Streaming: A Comprehensive Review of Network Congestion Models and Integration Strategies

As the demand for high-quality video streaming continues to surge, adaptive streaming algorithms play a pivotal role in ensuring an optimal viewing experience under varying network conditions. This review paper explores the intersection of adaptive video streaming and network congestion models, aiming to provide a comprehensive understanding of how mathematical models can simulate and predict network congestion scenarios. We delve into the evolution of video streaming technologies, highlighting the challenges posed by fluctuating network conditions and the motivation for developing adaptive streaming algorithms. The core focus of this paper lies in elucidating various network congestion models, presenting an in-depth analysis of mathematical frameworks, parameters, and variables employed in these models. Additionally, we provide an overview of existing adaptive streaming algorithms and discuss their ability to dynamically adjust to network conditions. Recognizing the limitations of traditional algorithms, we propose the integration of network congestion models as a solution to enhance adaptive streaming performance. Through the exploration of case studies and experiments, we validate the effectiveness of network congestion models in predicting and mitigating congestion scenarios. We also address challenges and limitations associated with the integration, offering insights into scenarios where models may face constraints. Finally, we discuss future research directions, suggesting avenues for advancing the integration of network congestion models with adaptive streaming. The paper concludes by emphasizing the importance of this interdisciplinary approach and its potential to shape the future of video streaming technologies