Enhancing Adaptive Video Streaming Through AI-Driven Predictive Analytics for Network Conditions: A Comprehensive Review


  • Koffa Khan Department of Computing and Information Technology, University of the West Indies, St. Augustine, Trinidad and Tobago. https://orcid.org/0000-0001-7712-1124




Adaptive Video Streaming, Predictive Analytics, AI-driven Decision-making, Network Conditions, Bitrate Adaptation


 As the demand for high-quality video streaming continues to surge, the adaptability of streaming systems to dynamic and unpredictable network conditions becomes paramount. This review paper delves into the realm of adaptive video streaming, focusing on the integration of AI-driven predictive analytics to anticipate and optimize network conditions. The paper provides an extensive overview of existing adaptive streaming algorithms, highlighting the challenges posed by fluctuating network conditions. It explores the role of predictive analytics in mitigating these challenges, emphasizing the use of machine learning models and AI technologies. Through case studies and discussions on real-world implementations, the paper showcases how predictive analytics enhances the decision-making process in adaptive streaming systems, leading to improved bitrate adaptation and content delivery. Challenges and limitations associated with predictive analytics are scrutinized, paving the way for a comprehensive understanding of its implications. The integration of predictive analytics into adaptive streaming systems is examined, emphasizing its potential to revolutionize the quality of service. Finally, the paper outlines future trends and research directions, offering insights into the evolving landscape of adaptive video streaming. This review consolidates knowledge and provides a valuable resource for researchers, practitioners, and industry professionals involved in the intersection of video streaming, predictive analytics, and artificial intelligence.


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How to Cite

Khan, K. (2024). Enhancing Adaptive Video Streaming Through AI-Driven Predictive Analytics for Network Conditions: A Comprehensive Review. International Transactions on Electrical Engineering and Computer Science, 3(1), 57–68. https://doi.org/10.62760/iteecs.3.1.2024.67