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Total Size:
6.0 MB
Info Hash:
68483F1D76389B2640625CBE49285BD68E829771
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April 20, 2026, 8:11 p.m.
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(Last updated: April 20, 2026, 8:16 p.m.)
| File | Size |
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| Darwich M. Enhancing Video Streaming with AI, Cloud, and Edge Technologies..2025.pdf | 6.0 MB |
NOTE
SOURCE: Darwich M. Enhancing Video Streaming with AI, Cloud, and Edge Technologies..2025
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COVER
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MEDIAINFO
Textbook in PDF format This book explores how Artificial Intelligence (AI), cloud computing, and edge technologies are transforming video streaming systems. It delves into AI-driven adaptive bitrate streaming, predictive resource allocation, and Federated Learning for personalized recommendations. The integration of cloud and edge computing is highlighted as a solution for scalability and low-latency streaming, addressing challenges like bandwidth optimization, cost-efficiency, and Quality of Experience (QoE). The book offers actionable insights into emerging technologies like 5G, quantum computing, and blockchain. It features case studies and real-world implementations, making it an essential resource for researchers, industry professionals, and students. Bridging theory and practice, the book provides a comprehensive guide to building the next generation of efficient and scalable video streaming infrastructures. The shift toward immersive experiences, such as virtual reality (VR) and augmented reality (AR), further compounds these challenges by adding new layers of complexity to content delivery. Streaming high-quality VR content, for instance, requires ultra-low latency and substantial bandwidth, making traditional centralized content delivery approaches increasingly insufficient. This book delves into the intersection of video streaming with cutting-edge technologies such as artificial intelligence (AI), cloud computing, and edge computing, which together offer innovative solutions to these challenges. AI has shown tremendous promise in enhancing video streaming performance through automated quality assessments, adaptive bitrate streaming, and real-time optimization of content delivery networks (CDNs). For example, machine learning models can predict network congestion and adjust streaming quality dynamically, ensuring a smooth viewing experience under varying conditions. Additionally, AI-driven video enhancement techniques such as super-resolution can improve perceived video quality without requiring an increase in bandwidth. This book is intended for a broad audience, including Computer Science researchers, professionals in media technology, and students seeking a comprehensive understanding of video streaming systems. By combining theoretical advancements with real-world applications, the book serves as both a foundational guide and an inspirational resource for exploring the evolving domain of video streaming. Preface Part I Foundations and Challenges in Video Streaming Introduction to Video Streaming Systems and Challenges Part II AI-Driven Approaches for Video Streaming AI-Driven Video Quality Assessment and Enhancement Techniques Federated Learning for Scalable Video Streaming Deep Learning for Adaptive Video Quality Part III Cloud and Edge Computing in Video Streaming Cloud-Enhanced Video Streaming: Storage and Resource Management Edge Computing for Low-Latency Video Streaming Swarm Intelligence for Efficient Video Data Distribution in Edge Networks Part IV Emerging Technologies in Video Streaming Blockchain-Enhanced Distributed Storage for Cloud-Based Video Streaming AI-Driven Resource Allocation and Optimization in Video Streaming Part V Practical Implementations and Future Trends Case Studies and Real-World Implementations of AI, Cloud, and Edge in Video Streaming Conclusion and Future Directions for Video Streaming Enhancements
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