東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Enhancing video streaming with AI, cloud, and edge technologies = optimization techniques and frameworks /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Enhancing video streaming with AI, cloud, and edge technologies/ by Mahmoud Darwich, Magdy Bayoumi.
其他題名:	optimization techniques and frameworks /
作者:	Darwīsh, Maḥmūd.
其他作者:	Bayoumi, Magdy A.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xxiii, 338 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Streaming video. -
電子資源:	https://doi.org/10.1007/978-3-031-84651-9
ISBN:	9783031846519

Enhancing video streaming with AI, cloud, and edge technologies = optimization 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

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

1458464
Streaming video.

LC Class. No.: TK6680.5

Dewey Class. No.: 006.74

Enhancing video streaming with AI, cloud, and edge technologies = optimization techniques and frameworks /
LDR:03035nmm a2200325 a 4500 001 2408630
003 DE-He213
005 20250328115303.0
006 m d
007 cr nn 008maaau
008 260204s2025 sz s 0 eng d
020 $a 9783031846519 $q (electronic bk.)
020 $a 9783031846502 $q (paper)
024 7 $a 10.1007/978-3-031-84651-9 $2 doi
035 $a 978-3-031-84651-9
040 $a GP $c GP
041 0 $a eng
050 4 $a TK6680.5
072 7 $a UG $2 bicssc
072 7 $a COM034000 $2 bisacsh
072 7 $a UG $2 thema
082 0 4 $a 006.74 $2 23
090 $a TK6680.5 $b .D228 2025
100 1 $a Darwīsh, Maḥmūd. $3 3781257
245 1 0 $a Enhancing video streaming with AI, cloud, and edge technologies $h [electronic resource] : $b optimization techniques and frameworks / $c by Mahmoud Darwich, Magdy Bayoumi.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xxiii, 338 p. : $b ill. (some col.), digital ; $c 24 cm.
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.
650 0 $a Streaming video. $2 gtt $3 1458464
650 0 $a Digital video $x Editing $x Data processing. $3 571521
650 0 $a Artificial intelligence. $3 516317
650 0 $a Cloud computing. $3 1016782
650 0 $a Edge computing. $3 3489844
650 1 4 $a Multimedia Information Systems. $3 892521
650 2 4 $a Cloud Computing. $3 3231328
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Bayoumi, Magdy A. $3 649201
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-84651-9
950 $a Computer Science (SpringerNature-11645)