Quality of Experience for Multimedia

Application to Content Delivery Network Architecture

Inbunden, Engelska, 2013

AvAbdelhamid Mellouk,Hai Anh Tran,Said Hoceini

2 289 kr

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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 AuthorsAbdelhamid 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.

Produktinformation

Utgivningsdatum2013-10-25
Mått163 x 241 x 20 mm
Vikt449 g
FormatInbunden
SpråkEngelska
Antal sidor176
FörlagISTE Ltd and John Wiley & Sons Inc
ISBN9781848215634

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List of Figures ixPreface xiiiIntroduction xvChapter 1 Network Control Based on Smart Communication Paradigm 11.1. Motivation 11.2. General framework 31.3. Main innovations 61.3.1. User perception metrics and affective computing 61.3.2. Knowledge dissemination 81.3.3. Bio-inspired approaches and control theory 91.4. Conclusion 10Chapter 2 Quality of Experience 112.1. Motivation 112.2. QoE concept 122.3. Importance of QoE 142.4. QoE metrics 162.5. QoE measurement methods 202.6. QoS/QoE relationship 232.7. Impact of networking on QoE 262.7.1. Layered classification of impacts on QoE 262.7.2. Impact of user mobility on QoE 282.7.3. Impact of network resource utilization and management on QoE 292.7.4. Impact of billing and pricing 302.8. Conclusion 31Chapter 3 Content Distribution Network 333.1. Motivation 333.2. Routing layer 363.2.1. Routing in telecommunication network 363.2.2. Classical routing algorithms 373.2.3. QoS-based routing 383.3. Meta-routing layer 423.3.1. Server placement 433.3.2. Cache organization 453.3.3. Server selection 473.4. Conclusion 49Chapter 4 User-driven Routing Algorithm Application for CDN Flow 514.1. Introduction 514.2. Reinforcement learning and Q-routing 534.2.1. Mathematical model of reinforcement learning 564.2.2. Value functions 574.3. Q-learning 604.4. Q-routing 614.5. Related works and motivation 624.6. QQAR routing algorithm 634.6.1. Formal parametric model 644.6.2. QQAR algorithm 654.6.3. Learning process 684.6.4. Simple use case-based example of QQAR 714.6.5. Selection process 784.7. Experimental results 794.7.1. Simulation setup 794.7.2. Experimental setup 894.7.3. Average MOS score 904.7.4. Convergence time 974.7.5. Capacity of convergence and fault tolerance 1004.7.6. Control overheads 1024.7.7. Packet delivery ratio 1034.8. Conclusion 104Chapter 5 User-driven Server Selection Algorithm for CDN Architecture 1055.1. Introduction 1055.2. Multi-armed bandit formalization 1085.2.1. MAB paradigm 1085.2.2. Applications of MAB 1125.2.3. Algorithms for MAB 1135.3. Server selection schemes 1195.4. Our proposal for QoE-based server selection method 1225.4.1. Proposed server selection scheme 1225.4.2. Proposed UCB1-based server selection algorithm 1255.5. Experimental results 1265.5.1. Simulation results 1265.5.2. Real platform results 1325.6. Acknowledgment 1335.7. Conclusion 135Conclusion 137Bibliography 141Index 155