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Quality of Experience for Multimedia: Application to Content Delivery Network Architecture [Kõva köide]

(University of Paris-Est, France), (University of Paris-Est, France), (University of Paris-Est, France)
  • Formaat: Hardback, 176 pages, kõrgus x laius x paksus: 241x163x20 mm, kaal: 449 g
  • Ilmumisaeg: 25-Oct-2013
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1848215630
  • ISBN-13: 9781848215634
  • Formaat: Hardback, 176 pages, kõrgus x laius x paksus: 241x163x20 mm, kaal: 449 g
  • Ilmumisaeg: 25-Oct-2013
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1848215630
  • ISBN-13: 9781848215634
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.

List of Figures
ix
Preface xiii
Introduction xv
Chapter 1 Network Control Based on Smart Communication Paradigm
1(10)
1.1 Motivation
1(2)
1.2 General framework
3(3)
1.3 Main innovations
6(4)
1.3.1 User perception metrics and affective computing
6(2)
1.3.2 Knowledge dissemination
8(1)
1.3.3 Bio-inspired approaches and control theory
9(1)
1.4 Conclusion
10(1)
Chapter 2 Quality of Experience
11(22)
2.1 Motivation
11(1)
2.2 QoE concept
12(2)
2.3 Importance of QoE
14(2)
2.4 QoE metrics
16(4)
2.5 QoE measurement methods
20(3)
2.6 QoS/QoE relationship
23(3)
2.7 Impact of networking on QoE
26(5)
2.7.1 Layered classification of impacts on QoE
26(2)
2.7.2 Impact of user mobility on QoE
28(1)
2.7.3 Impact of network resource utilization and management on QoE
29(1)
2.7.4 Impact of billing and pricing
30(1)
2.8 Conclusion
31(2)
Chapter 3 Content Distribution Network
33(18)
3.1 Motivation
33(3)
3.2 Routing layer
36(6)
3.2.1 Routing in telecommunication network
36(1)
3.2.2 Classical routing algorithms
37(1)
3.2.3 QoS-based routing
38(4)
3.3 Meta-routing layer
42(7)
3.3.1 Server placement
43(2)
3.3.2 Cache organization
45(2)
3.3.3 Server selection
47(2)
3.4 Conclusion
49(2)
Chapter 4 User-Driven Routing Algorithm Application For CDN Flow
51(54)
4.1 Introduction
51(2)
4.2 Reinforcement learning and Q-routing
53(7)
4.2.1 Mathematical model of reinforcement learning
56(1)
4.2.2 Value functions
57(3)
4.3 Q-learning
60(1)
4.4 Q-routing
61(1)
4.5 Related works and motivation
62(1)
4.6 QQAR routing algorithm
63(16)
4.6.1 Formal parametric model
64(1)
4.6.2 QQAR algorithm
65(3)
4.6.3 Learning process
68(3)
4.6.4 Simple use case-based example of QQAR
71(7)
4.6.5 Selection process
78(1)
4.7 Experimental results
79(25)
4.7.1 Simulation setup
79(10)
4.7.2 Experimental setup
89(1)
4.7.3 Average MOS score
90(7)
4.7.4 Convergence time
97(3)
4.7.5 Capacity of convergence and fault tolerance
100(2)
4.7.6 Control overheads
102(1)
4.7.7 Packet delivery ratio
103(1)
4.8 Conclusion
104(1)
Chapter 5 User-Driven Server Selection Algorithm for CDN Architecture
105(32)
5.1 Introduction
105(3)
5.2 Multi-armed bandit formalization
108(11)
5.2.1 MAB paradigm
108(4)
5.2.2 Applications of MAB
112(1)
5.2.3 Algorithms for MAB
113(6)
5.3 Server selection schemes
119(3)
5.4 Our proposal for QoE-based server selection method
122(4)
5.4.1 Proposed server selection scheme
122(3)
5.4.2 Proposed UCB1-based server selection algorithm
125(1)
5.5 Experimental results
126(7)
5.5.1 Simulation results
126(6)
5.5.2 Real platform results
132(1)
5.6 Acknowledgment
133(2)
5.7 Conclusion
135(2)
Conclusion 137(4)
Bibliography 141(14)
Index 155
Abdelhamid Mellouk, UPEC, LiSSi Lab, Paris -Est University, Paris, France.

Hai Anh Tran, UPEC, LiSSi Lab, Paris -Est University, Paris, France.

Said Hoceini, UPEC, LiSSi Lab, Paris -Est University, Paris, France.