Adaptive Bit Rate Streaming

For more info about our services contact : help@bestpfe.com

Table of contents

1 Introduction 
1.1 Motivation
1.2 Live Streaming Services Challenges
1.3 Summary of Contributions
1.4 Thesis Organization
1.5 List of Publications
2 State of the Art of Live Streaming Systems 
2.1 Introduction
2.2 Background
2.3 Live Video Streaming Traffic Studies
2.4 User-Generated Content
2.5 Multimedia Delivery Architectures
2.5.1 Video Delivery Models
2.5.2 Composing Hybrid Delivery Models
2.6 Video Transcoding
2.7 Adaptive Bit Rate Streaming
2.8 Conclusion
3 Live Streaming Sessions Data Set 
3.1 Introduction
3.2 Live Streaming Providers
3.2.1 Twitch
3.2.2 YouTube Live
3.3 Data Retrieval
3.4 Filters Used to Clean Up Traces
3.5 Status of Live Streaming Services
3.5.1 How Big are the Systems?
3.5.2 Are they 24/7 Services?
3.5.3 Zipf’s Law in UGC Live Streaming
3.6 Identifying Popular Broadcasters Sessions
3.6.1 Broadcasters Characteristics
3.6.2 Video Quality and Popularity
3.7 Conclusion
4 Mapping Sessions to Servers 
4.1 Introduction
4.2 Model
4.3 Mapping live video sessions on broadcasting servers
4.3.1 Popularity predictability discussion
4.3.2 Number of servers versus bandwidth usage trade-off
4.3.3 Taking video sessions popularity into account
4.4 Evaluation
4.5 Conclusion
5 Mixing Data Center and CDN for Delivery 
5.1 Introduction
5.2 Model for Hybrid Delivery
5.3 Theoretical Optimization Problem
5.4 Motivations for Hybrid Delivery
5.5 Evaluation
5.6 Conclusion
6 Transcoding for Adaptive Streaming 
6.1 Introduction
6.2 DASH Sessions Data Set
6.3 Which Channels to Transcode
6.3.1 Trade-off and Problem Definition
6.3.2 An On-the-Fly Strategy
6.3.3 An At-Startup Strategy
6.4 Evaluation
6.4.1 Settings
6.4.2 Evaluations
6.4.3 Playing with Strategies Parameters
6.5 Conclusion
7 Conclusion 
7.1 Synthesis
7.1.1 Live Sessions Data Set
7.1.2 Cloud Delivery
7.1.3 Hybrid Delivery
7.1.4 DASH on Live Streaming
7.1.5 Data Set Applications
7.1.6 Additional Contributions
7.2 Perspectives
7.2.1 Model Extension
7.2.2 Statistical and Learning Mechanisms
7.2.3 Middleware Integration
A Algorithms in Pseudo-code 
B Résumé Étendu en Français 
B.1 Introduction
B.2 Service de diffusion directe de vidéo en ligne
B.3 Contributions
B.3.1 L’ensemble de données des sessions en direct
B.3.2 Livraison par le nuage
B.3.3 Livraison hybride
B.3.4 Direct avec DASH
B.3.5 Applications de l’ensemble de données
B.3.6 Contributions additionnel
B.4 Conclusion
C Live Sessions Data Set Applications 
C.1 Introduction
C.2 CDN Fairness on Live Delivery
C.2.1 Introduction
C.2.2 Model
C.2.3 Maximizing the CDN revenue
C.2.4 Analysis
C.2.5 Conclusion
C.3 Transcoding Live Adaptive Video Streams in the Cloud
C.3.1 Introduction
C.3.2 Current Industrial Strategies
C.3.3 Transcoding CPU and PSNR Data Set
C.3.4 Optimizing Stream Preparation
C.3.5 A Heuristic Algorithm
C.3.6 Conclusion
C.4 Appendix Conclusion
Bibliography