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2025 | Buch

Enhancing Video Streaming with AI, Cloud, and Edge Technologies

Optimization Techniques and Frameworks

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Über dieses Buch

Dieses Buch untersucht, wie künstliche Intelligenz, Cloud Computing und Spitzentechnologien Video-Streaming-Systeme verändern. Es befasst sich mit adaptivem KI-basiertem Bitrate-Streaming, vorausschauender Ressourcenallokation und föderalem Lernen für personalisierte Empfehlungen. Die Integration von Cloud und Edge Computing wird als Lösung für Skalierbarkeit und Streaming mit geringer Latenz hervorgehoben, die Herausforderungen wie Bandbreitenoptimierung, Kosteneffizienz und Erfahrungsqualität (Quality of Experience, QoE) angeht. Das Buch bietet umsetzbare Einblicke in aufstrebende Technologien wie 5G, Quantencomputing und Blockchain. Es enthält Fallstudien und Implementierungen aus der realen Welt, was es zu einer unverzichtbaren Ressource für Forscher, Fachleute aus der Industrie und Studenten macht. Als Brücke zwischen Theorie und Praxis bietet das Buch einen umfassenden Leitfaden zum Aufbau der nächsten Generation effizienter und skalierbarer Video-Streaming-Infrastrukturen.

Inhaltsverzeichnis

Frontmatter

Part I

Frontmatter
Chapter 1. Introduction to Video Streaming Systems and Challenges
Abstract
This chapter introduces video streaming technologies, focusing on the key challenges of scalability, Quality of Experience (QoE), and cost-efficiency in modern systems. It examines how emerging technologies such as artificial intelligence (AI), cloud computing, and edge computing address these challenges by optimizing video delivery and user experience. As the demand for real-time, high-quality content continues to grow, these technologies play a critical role in meeting scalability requirements while maintaining performance and cost-effectiveness. The chapter also provides an overview of the book’s structure, highlighting the themes and topics discussed in subsequent chapters, which cover specific AI-driven techniques, cloud and edge integration, and real-world implementations for optimizing video streaming systems. This chapter serves as a foundation for understanding the transformative potential of these technologies in enhancing modern video streaming infrastructures.
Mahmoud Darwich, Magdy Bayoumi

Part II

Frontmatter
Chapter 2. AI-Driven Video Quality Assessment and Enhancement Techniques
Abstract
This chapter delves into the advanced application of Artificial Intelligence (AI) for video quality assessment and enhancement, crucial for improving real-time video streaming experiences. Techniques such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and reinforcement learning are explored in depth, showcasing how these AI methods contribute to dynamic resolution adjustment, noise reduction, and adaptive bitrate streaming. The chapter highlights CNN-based approaches to video quality assessment, focusing on multi-scale analysis for detecting subtle quality degradations in real time. Additionally, GANs are discussed for their role in video upscaling, artifact removal, and noise reduction, providing insights into how AI enhances low-quality video into high-definition formats. Reinforcement learning, applied in adaptive bitrate streaming, is examined for its ability to optimize video delivery under fluctuating network conditions. The chapter is supported by real-world case studies demonstrating the tangible benefits of these AI-driven techniques in improving user satisfaction, reducing latency, and enhancing service efficiency. By integrating AI across various stages of video streaming, platforms can deliver consistently high-quality video, ensuring seamless user experiences even in challenging network environments.
Mahmoud Darwich, Magdy Bayoumi
Chapter 3. Federated Learning for Scalable Video Streaming
Abstract
This chapter investigates the application of Federated Learning (FL) in decentralized video streaming systems, focusing on how FL enhances scalability while preserving user privacy. Traditional centralized video streaming architectures face challenges such as bandwidth constraints, privacy risks, and server-side processing limitations as demand continues to grow. FL addresses these issues by enabling local devices to collaboratively train machine learning models without exchanging raw data. Key topics include data partitioning strategies, model aggregation techniques, and the trade-offs between scalability, efficiency, and privacy in FL-based streaming systems. Additionally, we present real-world case studies that demonstrate how FL can be effectively deployed to optimize video streaming performance, particularly in areas like personalized content recommendations and adaptive bitrate streaming. These examples highlight the benefits of decentralized learning in achieving high-quality, scalable, and privacy-respecting video streaming services.
Mahmoud Darwich, Magdy Bayoumi
Chapter 4. Deep Learning for Adaptive Video Quality
Abstract
This chapter explores the application of deep learning techniques, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), in adaptive video quality optimization for large-scale streaming platforms. With the growing demand for high-quality, uninterrupted video streaming, deep learning has emerged as a powerful tool to improve Quality of Experience (QoE) by dynamically adjusting video quality in real time. Traditional rule-based adaptive bitrate (ABR) algorithms are often inadequate in dealing with fluctuating network conditions, leading to suboptimal user experiences. In contrast, deep learning models are capable of analyzing both video content and network conditions to optimize video quality, reduce latency, and personalize the streaming experience for individual users. This chapter highlights how CNNs process spatial information within video frames to optimize visual quality, while RNNs address temporal aspects by predicting network performance and adjusting quality accordingly. Several case studies are also examined, showcasing the effectiveness of deep learning in minimizing buffering and improving overall QoE in various streaming scenarios. Through detailed exploration of architecture, applications, and real-world implementations, this chapter provides a comprehensive understanding of how deep learning can transform the video streaming landscape.
Mahmoud Darwich, Magdy Bayoumi

Part III

Frontmatter
Chapter 5. Cloud-Enhanced Video Streaming: Storage and Resource Management
Abstract
This chapter delves into the critical role cloud computing plays in enhancing the scalability, storage management, and resource allocation for large-scale video streaming services. As demand for high-quality streaming continues to grow, traditional infrastructure models struggle to manage the bandwidth, storage, and computational resources required to deliver a seamless user experience. Cloud computing offers a flexible and scalable solution to these challenges, enabling streaming platforms to dynamically allocate resources based on demand. This chapter explores machine learning-based predictive storage management techniques using ARIMA and LSTM models, which are used to forecast storage needs based on historical usage patterns and real-time data. Additionally, it discusses cloud resource allocation strategies for managing bandwidth, CPU, and storage resources in an optimized manner. Finally, the chapter analyzes the cost-efficiency trade-offs of cloud-enhanced streaming, including considerations around operational costs, quality of service (QoS), and performance optimization. Through a combination of theoretical analysis, case studies, and real-world examples, this chapter provides insights into how cloud computing can transform the video streaming landscape by optimizing storage and resource management at scale.
Mahmoud Darwich, Magdy Bayoumi
Chapter 6. Edge Computing for Low-Latency Video Streaming
Abstract
Edge computing is emerging as a pivotal technology in enhancing video streaming services by reducing latency and optimizing bandwidth usage. This chapter explores the key roles that edge computing plays in addressing the challenges of delivering high-quality, real-time video content, particularly in remote and bandwidth-constrained regions. Techniques such as edge-based video caching and computation offloading are examined to illustrate how processing closer to end-users can significantly improve video distribution efficiency. By storing popular content at edge nodes and performing computationally intensive tasks such as video encoding and transcoding locally, platforms can reduce the burden on central servers and lower the data transmission load across the network. The chapter also delves into the use of edge computing in geographically remote areas, where traditional cloud infrastructure may struggle to meet performance requirements. Through a detailed analysis of the benefits of edge computing, this chapter demonstrates its critical role in future video streaming architectures, enabling scalable, low-latency content delivery while optimizing costs and enhancing the overall user experience.
Mahmoud Darwich, Magdy Bayoumi
Chapter 7. Swarm Intelligence for Efficient Video Data Distribution in Edge Networks
Abstract
Swarm intelligence, inspired by natural systems such as ant colonies and bird flocking, offers a powerful approach to optimizing video data distribution in edge networks. As video streaming services expand, the need for efficient bandwidth usage and low-latency delivery becomes critical, particularly in edge-driven environments. This chapter explores the application of swarm intelligence algorithms, such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), to enhance video streaming performance in edge networks. These algorithms help in dynamically managing network resources, optimizing video routing, and reducing delays in video transmission. The role of edge computing in improving video quality, especially in remote and bandwidth-limited regions, is also discussed. Edge-based video caching and computation offloading are highlighted as key techniques that, when combined with swarm intelligence, can greatly improve the Quality of Service (QoS) in real-time video delivery. Through case studies and performance evaluations, the chapter demonstrates how swarm intelligence can effectively reduce network congestion, improve load balancing, and ensure scalable, high-quality streaming experiences. As demand for high-definition video content continues to grow, swarm intelligence is poised to play a pivotal role in the future of edge-based video streaming.
Mahmoud Darwich, Magdy Bayoumi

Part IV

Frontmatter
Chapter 8. Blockchain-Enhanced Distributed Storage for Cloud-Based Video Streaming
Abstract
This chapter explores the integration of blockchain technology with distributed storage to enhance cloud-based video streaming. The rising demand for high-quality video content presents challenges in scalability, security, and data consistency that traditional centralized cloud architectures struggle to meet. Blockchain’s decentralized and immutable structure offers a solution by providing improved data security, enhanced scalability, and transparency in distributed storage systems. Key topics covered include the role of blockchain in addressing limitations of conventional streaming infrastructures through consensus algorithms, smart contracts, and decentralized networks. Case studies illustrate blockchain’s potential in reducing costs, optimizing bandwidth, and securing content delivery. The chapter also discusses current limitations, such as scalability and regulatory concerns, while highlighting future trends like decentralized finance (DeFi), non-fungible tokens (NFTs), and layer-2 scaling solutions. By leveraging blockchain’s decentralized nature, video streaming platforms can achieve more efficient, secure, and scalable content distribution. The chapter concludes that as blockchain technologies mature, their adoption will play a pivotal role in transforming cloud-based video streaming systems.
Mahmoud Darwich, Magdy Bayoumi
Chapter 9. AI-Driven Resource Allocation and Optimization in Video Streaming
Abstract
This chapter explores the application of AI-driven approaches for optimizing resource allocation in video streaming systems, focusing on the dynamic management of cloud and edge resources. Techniques such as reinforcement learning, deep Q-networks, and predictive analytics are discussed as key enablers for minimizing costs, reducing latency, and improving the Quality of Experience (QoE) for users. AI algorithms allow streaming platforms to balance workloads across cloud and edge environments, adaptively allocating computing power, bandwidth, and storage based on real-time network conditions. Case studies demonstrate significant performance gains achieved through AI-based resource management, including cost reductions and improved streaming quality. The chapter also addresses challenges related to scalability, model accuracy, and data availability in AI implementations. Future trends such as federated learning and the integration of supporting technologies like 5G and blockchain are highlighted as potential avenues for enhancing resource optimization in the evolving landscape of video streaming. The presented insights offer a comprehensive understanding of how AI techniques can transform cloud and edge resource management, paving the way for more efficient, scalable, and cost-effective video delivery systems.
Mahmoud Darwich, Magdy Bayoumi

Part V

Frontmatter
Chapter 10. Case Studies and Real-World Implementations of AI, Cloud, and Edge in Video Streaming
Abstract
This chapter presents practical case studies and real-world implementations of artificial intelligence (AI), cloud computing, and edge computing in video streaming. It explores how these technologies have been deployed to optimize video delivery, improve scalability, and enhance user experiences across platforms such as Netflix, YouTube, and Amazon Prime Video. The case studies highlight AI-driven content recommendation systems, adaptive bitrate streaming, and cloud-based resource management. Additionally, the chapter emphasizes the role of edge computing in reducing latency for real-time applications like live streaming and interactive video experiences. Through these examples, the chapter provides valuable insights into the challenges, best practices, and future directions for implementing AI, cloud, and edge technologies in video streaming systems.
Mahmoud Darwich, Magdy Bayoumi
Chapter 11. Conclusion and Future Directions for Video Streaming Enhancements
Abstract
The concluding chapter synthesizes the key insights from the various technologies and methodologies explored throughout the book, with a focus on artificial intelligence (AI), cloud computing, and edge computing, and their transformative role in modernizing video streaming systems. This chapter revisits key challenges such as improving Quality of Experience (QoE), minimizing latency, and enhancing scalability. Furthermore, the chapter explores emerging technologies like 6G networks, quantum computing, and blockchain, which promise to revolutionize video delivery through ultra-low latency, efficient video analytics, and secure decentralized architectures. By examining practical applications and future directions, the chapter sets the stage for continued innovation in video streaming technologies.
Mahmoud Darwich, Magdy Bayoumi
Backmatter
Metadaten
Titel
Enhancing Video Streaming with AI, Cloud, and Edge Technologies
verfasst von
Mahmoud Darwich
Magdy Bayoumi
Copyright-Jahr
2025
Electronic ISBN
978-3-031-84651-9
Print ISBN
978-3-031-84650-2
DOI
https://doi.org/10.1007/978-3-031-84651-9