Quality of Experience Engineering for Customer Added Value Services

Name: Quality of Experience Engineering for Customer Added Value Services | From Evaluation to Monitoring
Brand: Wiley-ISTE
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From Evaluation to Monitoring

Abdelhamid Mellouk Antonio Cuadra-Sanchez(Editor)

Wiley-ISTE (Publisher)

Published on 9. July 2014

256 pages

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978-1-118-98434-5 (ISBN)

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Content

Cover
Title Page
Copyright
Contents
Preface
Chapter 1. Challenges for Quality of Experience Engineering for Added Value Services
1.1. Introduction and challenges
1.2. Contents
1.2.1. Chapter 2: An Ecosystem for customer experience management
1.2.2. Chapter 3: Measuring MPEG frame loss rate to evaluate the QoE in IPTV services
1.2.3. Chapter 4: Estimating the effect of context on the QoE of audiovisual services
1.2.4. Chapter 5: IPTV multiservice QOE management system
1.2.5. Chapter 6: High speed multimedia flow classification
1.2.6. Chapter 7: User driven server Selection algorithm for CDN architecture
1.2.7. Chapter 8: QoE approaches for adaptive transport of video streaming media
1.2.8. Chapter 9: QoS and QoE effects of packet losses in multimedia video streaming
1.2.9. Chapter 10: A model for QoE estimation based on QoS parameters monitoring for multimedia convergent services (triple play)
1.2.10. Chapter 11: Quality of experience estimators in networks
1.2.11. Chatper 12: QoE-based network selection is heterogeneous environments
1.3. Conclusion
Chapter 2. An Ecosystem For Customer Experience Management
2.1. Introduction
2.2. Managing customer experience
2.2.1. Customer experience management
2.2.2. Service quality management
2.3. Quality of experience ecosystem
2.3.1. QoE Data sources
2.3.2. QoE monitoring system
2.3.3. QoE management system
2.4. IPNQSIS
2.5. NOTTS
2.6. Conclusions
2.7. Acknowledgments
2.8. Bibliography
Chapter 3. Measuring Mpeg Frame Loss Rate to Evaluate the Quality of Experience in Iptv Services
3.1. Introduction
3.2. Related work
3.3. Method description
3.3.1. Video setup
3.3.2. Experiment data
3.3.3. VQM general model
3.4. QoE prediction models
3.4.1. Packet loss rate based model
3.4.2. MPEG frame types
3.4.3. MPEG frame loss model
3.5. Network monitoring tool
3.5.1. Flow Monitor
3.5.2. Capture Analyzer
3.5.3. QoE Estimator
3.6. Performance assessment
3.6.1. Assessment on Intel-based PC
3.6.2. Assessment on an ARM-based device
3.7. Conclusions and future work
3.8. Acknowledgments
3.9. Bibliography
Chapter 4. Estimating The Effect of Context on the Qoe of Audiovisual Services
4.1. Introduction
4.2. Test content
4.3. Subjective tests in laboratory
4.4. Subjective tests at exhibition
4.5. Results
4.6. Conclusions and further work
4.7. Bibliography
Chapter 5. Iptv Multiservice Qoe Management System
5.1. Introduction
5.2. State of the art
5.2.1. Video quality metrics
5.2.2. Multimedia quality metrics
5.2.3. Metrics for QoE monitoring of video services
5.3. Multiservice IPTV probe
5.3.1. Architecture of the IPTV probe
5.3.2. Video quality metrics applied in the IPTV probe
5.4. QoE management system
5.4.1. Requirements for monitoring and management of QoE
5.4.2. Configuration management
5.4.3. Result management
5.5. Conclusions
5.6. Acknowledgments
5.7. Bibliography
Chapter 6. High Speed Multimedia Flow Classification
6.1. Introduction
6.2. The architecture
6.2.1. Intel DPDK
6.2.2. Deep packet inspection over GPU
6.3. Validation
6.3.1. Used hardware
6.3.2. Used traffic traces
6.3.3. Obtained results
6.4. Conclusions
6.5. Acknowledgments
6.6. Bibliography
Chapter 7. User Driven Server Selection Algorithm for Cdn Architecture
7.1. Introduction
7.2. Multi-armed bandit formalization
7.2.1. MAB paradigm
7.2.2. Applications of MAB
7.2.3. Algorithms for MAB
7.3. Server selection schemes
7.4. Our proposal for QoE-based server selection method
7.4.1. Proposed server selection scheme
7.4.2. Proposed UCB1-based server selection algorithm
7.5. Experimental results
7.5.1. Simulation results
7.6. Acknowledgments
7.7. Conclusion
7.8. Bibliography
Chapter 8. Qoe Approaches for Adaptive Transport of Video Streaming Media
8.1. Introduction
8.2. Adaptive video transport
8.2.1. Background
8.2.2. Adaptive video delivery components
8.3. Microsoft Smooth Streaming
8.4. Apple HTTP live streaming
8.5. Adobe HTTP dynamic streaming
8.6. MPEG-dynamic adaptive streaming over HTTP
8.7. The goals of adaptive video streaming
8.8. Quality metrics for video streaming
8.9. The role of TCP in adaptive video streaming
8.10. Bibliography
Chapter 9. Qos And Qoe Effects of Packet Losses in Multimedia Video Streaming
9.1. Introduction to the overall scenario
9.2. Related work
9.3. Multilayer performance metrics
9.3.1. Overlay performance metrics
9.3.2. Application performance metrics
9.4. QoE multilayer metric and quality assessment mechanism
9.5. Video streaming use case: peer-to-peer television (P2PTV)
9.5.1. Testing environment
9.5.2. Performance evaluation
9.6. Conclusions and further actions
9.7. Bibliography
Chapter 10. A Model for Quality of Experience Estimation Based on Quality of Service Parameter Monitoring for Multimedia Convergent Services (3-Play)
10.1. Introduction
10.2. Presentation of the model
10.3. Application of the model to convergent (3P) services
10.4. Quality evaluation process
10.4.1. Global quality evaluation
10.4.2. Valuation and parameterization functions
10.4.3. Weighting and aggregation processes
10.4.4. Performance functions
10.5. Model testing
10.6. Conclusions and future work
10.7. Acknowledgments
10.8. Bibliography
Chapter 11. Quality of Experience Estimators in Networks
11.1. Introduction
11.2. QuEEN terminology and concepts
11.3. Modeling the QoE. The ARCU model
11.3.1. Four spaces for QoE-affecting factors
11.4. The QuEEN layered model
11.5. Applications
11.5.1. The QuEEN agent
11.5.2. Service QoE monitoring
11.5.3. QoE-driven network management
11.5.4. Service level management
11.6. Conclusions
11.7. Acknowledgments
11.8. Bibliography
Chapter 12. Qoe-Based Network Selection in Heterogeneous Environments
12.1. Introduction
12.2. Network selection in homogeneous environments: a use case in WLAN
12.3. Related work for network selection in the heterogeneous environment
12.4. QoE-based network selection in heterogeneous networks
12.4.1. Getting QoE in real-time
12.4.2. Using QoE in network selection over heterogeneous environment
12.4.3. Discussion of QoE and load-balancing results
12.5. Conclusions and discussions
12.6. Bibliography
List of Authors
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