Adaptive Horizons: Machine Learning Unveiling the Future of Video Streaming Optimization

This paper explores the pivotal role of machine learning (ML) algorithms in optimizing adaptive video streaming, aiming to enhance the quality of user experiences in multimedia content delivery. Traditional adaptive streaming methods face challenges in accommodating diverse network conditions, content characteristics, and user preferences. The paper focuses on three critical dimensions: bitrate adaptation, content prediction, and network condition optimization. ML algorithms dynamically adjust video bitrates based on historical data, user behavior, and real-time network conditions. Additionally, algorithms predict user preferences and content characteristics for personalized content delivery. The paper provides case studies illustrating successful applications of ML in adaptive video streaming, acknowledging challenges and limitations. Looking forward, it envisions future directions for ML in this domain, underscoring its transformative potential in shaping the future of streaming services. This exploration offers valuable insights into the synergy between machine learning and adaptive video streaming, paving the way for advancements in multimedia content delivery.