Enhancing Video Storage Efficiency in the Cloud: Machine Learning-Driven Optimization Strategies

Video storage optimization in the cloud is a critical research area with significant implications for efficient resource utilization, cost reduction, and improved user experiences. This research manuscript explores the application of machine learning techniques for video storage optimization in cloud-based environments. The objective is to develop innovative methods that leverage the power of machine learning algorithms to enhance the efficiency of video storage, enabling effective management, retrieval, and delivery of video content. The manuscript presents a comprehensive analysis of existing approaches, identifies their limitations, and proposes novel techniques to address the identified challenges. The research focuses on leveraging machine learning algorithms for video transcoding, content-aware caching, intelligent data placement, and adaptive streaming, among other areas. Experimental evaluations and performance comparisons are conducted to validate the effectiveness of the proposed techniques. The findings show that the proposed CNN-based video transcoding technique achieved an average storage size reduction of 40%. The content-aware caching technique outperformed baseline methods with an average cache hit rate of 75%. The RL-based intelligent data placement technique achieved an average data popularity of 85%. Lastly, the RL-based adaptive streaming technique obtained significantly higher user satisfaction ratings, with an average Mean Opinion Score (MOS) of 4.6 out of 5.