| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910992771403321 |
|
|
Autore |
Darwīsh Maḥmūd |
|
|
Titolo |
Enhancing Video Streaming with AI, Cloud, and Edge Technologies : Optimization Techniques and Frameworks / / by Mahmoud Darwich, Magdy Bayoumi |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2025.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (XXIII, 338 p. 107 illus., 106 illus. in color.) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Multimedia systems |
Cloud computing |
Artificial intelligence |
Multimedia Information Systems |
Cloud Computing |
Artificial Intelligence |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
Part I Foundations and Challenges in Video Streaming -- Chapter 1 Introduction to Video Streaming Systems and Challenges -- Part II AI-Driven Approaches for Video Streaming -- Chapter 2 AI-Driven Video Quality Assessment and Enhancement Techniques -- Chapter 3 Federated Learning for Scalable Video Streaming -- Chapter 4 Deep Learning for Adaptive Video Quality -- Part III Cloud and Edge Computing in Video Streaming -- Chapter 5 Cloud-Enhanced Video Streaming: Storage and Resource Management -- Chapter 6 Edge Computing for Low-Latency Video Streaming -- Chapter 7 Swarm Intelligence for Efficient Video Data Distribution in Edge Networks -- Part IV Emerging Technologies in Video Streaming -- Chapter 8 Blockchain-Enhanced Distributed Storage for Cloud-Based Video Streaming -- Chapter 9 AI-Driven Resource Allocation and Optimization in Video Streaming -- Part V Practical Implementations and Future Trends -- Chapter 10 Case Studies and Real-World Implementations of AI, Cloud, and Edge in Video Streaming -- Chapter 11 Conclusion and Future Directions for Video Streaming |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
This book explores how artificial intelligence, 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. |
|
|
|
|
|
|
|
| |