Cover image for Quality-of-Experience for Multimedia : Application to Content Delivery Network Architecture.
Quality-of-Experience for Multimedia : Application to Content Delivery Network Architecture.
Title:
Quality-of-Experience for Multimedia : Application to Content Delivery Network Architecture.
Author:
Mellouk, Abdelhamid.
ISBN:
9781118649374
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (178 pages)
Series:
FOCUS Series
Contents:
Cover -- Title page -- Contents -- LIST OF FIGURES -- PREFACE -- INTRODUCTION -- CHAPTER 1. NETWORK CONTROL BASED ON SMART COMMUNICATION PARADIGM -- 1.1. Motivation -- 1.2. General framework -- 1.3. Main innovations -- 1.3.1. User perception metrics and affective computing -- 1.3.2. Knowledge dissemination -- 1.3.3. Bio-inspired approaches and control theory -- 1.4. Conclusion -- CHAPTER 2. QUALITY OF EXPERIENCE -- 2.1. Motivation -- 2.2. QoE concept -- 2.3. Importance of QoE -- 2.4. QoE metrics -- 2.5. QoE measurement methods -- 2.6. QoS/QoE relationship -- 2.7. Impact of networking on QoE -- 2.7.1. Layered classification of impacts on QoE -- 2.7.2. Impact of user mobility on QoE -- 2.7.3. Impact of network resource utilization and management on QoE -- 2.7.4. Impact of billing and pricing -- 2.8. Conclusion -- CHAPTER 3. CONTENT DISTRIBUTION NETWORK -- 3.1. Motivation -- 3.2. Routing layer -- 3.2.1. Routing in telecommunication network -- 3.2.2. Classical routing algorithms -- 3.2.3. QoS-based routing -- 3.3. Meta-routing layer -- 3.3.1. Server placement -- 3.3.2. Cache organization -- 3.3.3. Server selection -- 3.4. Conclusion -- CHAPTER 4. USER-DRIVEN ROUTING ALGORITHM APPLICATION FOR CDN FLOW -- 4.1. Introduction -- 4.2. Reinforcement learning and Q-routing -- 4.2.1. Mathematical model of reinforcement learning -- 4.2.2. Value functions -- 4.3. Q-learning -- 4.4. Q-routing -- 4.5. Related works and motivation -- 4.6. QQAR routing algorithm -- 4.6.1. Formal parametric model -- 4.6.2. QQAR algorithm -- 4.6.3. Learning process -- 4.6.4. Simple use case-based example of QQAR -- 4.6.5. Selection process -- 4.7. Experimental results -- 4.7.1. Simulation setup -- 4.7.2. Experimental setup -- 4.7.3. Average MOS score -- 4.7.4. Convergence time -- 4.7.5. Capacity of convergence and fault tolerance -- 4.7.6. Control overheads.

4.7.7. Packet delivery ratio -- 4.8. Conclusion -- CHAPTER 5. USER-DRIVEN SERVER SELECTION ALGORITHM FOR CDN ARCHITECTURE -- 5.1. Introduction -- 5.2. Multi-armed bandit formalization -- 5.2.1. MAB paradigm -- 5.2.2. Applications of MAB -- 5.2.3. Algorithms for MAB -- 5.3. Server selection schemes -- 5.4. Our proposal for QoE-based server selection method -- 5.4.1. Proposed server selection scheme -- 5.4.2. Proposed UCB1-based server selection algorithm -- 5.5. Experimental results -- 5.5.1. Simulation results -- 5.5.2. Real platform results -- 5.6. Acknowledgment -- 5.7. Conclusion -- CONCLUSION -- BIBLIOGRAPHY -- INDEX.
Abstract:
Based on a convergence of network technologies, the Next Generation Network (NGN) is being deployed to carry high quality video and voice data. In fact, the convergence of network technologies has been driven by the converging needs of end-users.The perceived end-to-end quality is one of the main goals required by users that must be guaranteed by the network operators and the Internet Service Providers, through manufacturer equipment. This is referred to as the notion of Quality of Experience (QoE) and is becoming commonly used to represent user perception. The QoE is not a technical metric, but rather a concept consisting of all elements of a user's perception of the network services. The authors of this book focus on the idea of how to integrate the QoE into a control-command chain in order to construct an adaptive network system. More precisely, in the context of Content-Oriented Networks used to redesign the current Internet architecture to accommodate content-oriented applications and services, they aim to describe an end-to-end QoE model applied to a Content Distribution Network architecture. About the Authors Abdelhamid Mellouk is Full Professor at University of Paris-Est C-VdM (UPEC), Networks & Telecommunications (N&T) Department and LiSSi Laboratory, France. Head of several executive national and international positions, he was the founder of the Network Control Research activity at UPEC with extensive international academic and industrial collaborations. His general area of research is in adaptive real-time control for high-speed new generation dynamic wired/wireless networks in order to maintain acceptable Quality of Service/Experience for added-value services. He is an active member of the IEEE Communications Society and has held several offices including leadership positions in IEEE Communications Society Technical

Committees.Said Hoceini is Associate Professor at University of Paris-Est C-VdM (UPEC), Networks & Telecommunications (N&T) Department and LiSSi Laboratory, France. His research focuses on routing algorithms, quality of service, quality of experience, and wireless sensor networks, as well as bio-inspired artificial intelligence approaches. His work has been published in several international conferences and journals and he serves on several TPCs.Hai Anh Tran is Associate Professor at the Hanoi University of Science and Technology (HUST), Vietnam. His research focuses on QoE aspects, QoS adaptive control/command mechanisms, wired routing, as well as bio-inspired artificial intelligence approaches.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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