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Enhancing video streaming with AI, cloud, and edge technologiesoptimization techniques and frameworks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Enhancing video streaming with AI, cloud, and edge technologiesby Mahmoud Darwich, Magdy Bayoumi.
Reminder of title:	optimization techniques and frameworks /
Author:	Darwīsh, Maḥmūd.
other author:	Bayoumi, Magdy A.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xxiii, 338 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Streaming video.
Online resource:	https://doi.org/10.1007/978-3-031-84651-9
ISBN:	9783031846519$q(electronic bk.)

Enhancing video streaming with AI, cloud, and edge technologiesoptimization techniques and frameworks /
Darwīsh, Maḥmūd.

Enhancing video streaming with AI, cloud, and edge technologiesoptimization techniques and frameworks /[electronic resource] :by Mahmoud Darwich, Magdy Bayoumi. - Cham :Springer Nature Switzerland :2025. - xxiii, 338 p. :ill. (some col.), digital ;24 cm.

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 Enhancements.

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.

ISBN: 9783031846519$q(electronic bk.)

Standard No.: 10.1007/978-3-031-84651-9doiSubjects--Topical Terms:

538203
Streaming video.

LC Class. No.: TK6680.5

Dewey Class. No.: 006.74

Enhancing video streaming with AI, cloud, and edge technologiesoptimization techniques and frameworks /
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505 0 $a 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 Enhancements.
520 $a 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.
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