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Quality-of-experience for multimedia = application to content delivery network architecture /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Quality-of-experience for multimedia/ Abdelhamid Mellouk, Said Hoceini, Hai Anh Tran.
Reminder of title:	application to content delivery network architecture /
Author:	Mellouk, Abdelhamid.
other author:	Tran, Hai Anh.
Published:	Hoboken :Wiley, : 2013.,
Description:	1 online resource (178 p.)
Subject:	Computer network architectures. -
Online resource:	http://onlinelibrary.wiley.com/book/10.1002/9781118649367
ISBN:	9781118649367 (electronic bk.)

Quality-of-experience for multimedia = application to content delivery network architecture /
Mellouk, Abdelhamid.

Quality-of-experience for multimediaapplication to content delivery network architecture /[electronic resource] :Abdelhamid Mellouk, Said Hoceini, Hai Anh Tran. - Hoboken :Wiley,2013. - 1 online resource (178 p.) - FOCUS Series. - FOCUS Series..

Includes bibliographical references and index.

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

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 met.

ISBN: 9781118649367 (electronic bk.)Subjects--Topical Terms:

565372
Computer network architectures.

LC Class. No.: TK5103.2

Dewey Class. No.: 004.6

Quality-of-experience for multimedia = application to content delivery network architecture /
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100 1 $a Mellouk, Abdelhamid. $3 965595
245 1 0 $a Quality-of-experience for multimedia $h [electronic resource] : $b application to content delivery network architecture / $c Abdelhamid Mellouk, Said Hoceini, Hai Anh Tran.
260 $a Hoboken : $b Wiley, $c 2013.
300 $a 1 online resource (178 p.)
490 1 $a FOCUS Series
504 $a Includes bibliographical references and index.
505 0 $a 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
505 8 $a 2.7.1. Layered classification of impacts on QoE2.7.2. 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
505 8 $a 4.2. Reinforcement learning and Q-routing4.2.1. 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
505 8 $a 4.7.7. Packet delivery ratio4.8. 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
520 $a 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 met.
588 $a Description based upon print version of record.
650 0 $a Computer network architectures. $3 565372
700 1 $a Tran, Hai Anh. $3 1059337
700 1 $a Hoceini, Said. $3 1059338
830 0 $a FOCUS Series. $3 1058710
856 4 0 $u http://onlinelibrary.wiley.com/book/10.1002/9781118649367