Edge computing for real-time video streaming optimization – Complete Phd and Masters Thesis

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Introduction

In recent years, the demand for real-time video streaming has grown exponentially with the rise of live streaming platforms, video conferencing applications, and online gaming. However, traditional cloud-based video streaming services face challenges such as latency, bandwidth constraints, and network congestion, which can degrade the quality of the streaming experience for users.

Edge computing has emerged as a promising solution to optimize real-time video streaming by bringing computation and storage closer to the edge of the network, reducing latency and improving the overall streaming performance. This thesis aims to explore the potential of edge computing for real-time video streaming optimization and propose novel solutions to enhance the quality of experience for users.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of edge computing
2.2 Real-time video streaming technologies
2.3 Challenges in real-time video streaming optimization
2.4 Edge computing for video streaming optimization
2.5 Quality of Experience (QoE) metrics
2.6 Edge computing architectures for video streaming
2.7 Content delivery networks (CDNs) for video streaming
2.8 Edge caching techniques
2.9 Network optimization for real-time video streaming
2.10 Machine learning and AI for video streaming optimization

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 Experimental setup
3.5 Performance metrics
3.6 Simulation tools
3.7 Case studies
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Performance evaluation of edge computing for video streaming
4.2 Comparison of edge caching techniques
4.3 Impact of network optimization on video streaming quality
4.4 Machine learning algorithms for video streaming optimization
4.5 User feedback and subjective evaluations
4.6 Scalability and cost considerations
4.7 Future research directions

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Implications for industry
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview:

Edge computing has gained significant attention in recent years as a promising solution to optimize real-time video streaming. This thesis explores the potential of edge computing for video streaming optimization, focusing on reducing latency, improving streaming quality, and enhancing the overall user experience.

Chapter 1 provides an introduction to the research topic, background information, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms.

Chapter 2 reviews the existing literature on edge computing, real-time video streaming technologies, challenges in optimization, edge computing architectures, CDNs, edge caching techniques, network optimization, and machine learning for video streaming optimization.

Chapter 3 outlines the research methodology, including research design, data collection methods, data analysis techniques, experimental setup, performance metrics, simulation tools, case studies, and ethical considerations.

Chapter 4 discusses the findings of the research, including performance evaluations of edge computing, comparisons of edge caching techniques, the impact of network optimization, machine learning algorithms, user feedback, scalability considerations, and future research directions.

Chapter 5 presents the conclusion and summary of the thesis, summarizing the findings, discussing the contributions to the field, implications for industry, recommendations for future research, and concluding remarks.

Overall, this thesis aims to contribute to the growing body of knowledge on edge computing for real-time video streaming optimization and provide valuable insights for researchers, industry professionals, and policymakers in the field.

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