Quality of experience paradigm in multimedia services : application to OTT video streaming and VoIP services /

The analysis of QoE is not an easy task, especially for multimedia services, because all the factors (technical and non-technical) that directly or indirectly influence the user-perceived quality have to be considered. This book describes different methods to investigate users' QoE from the vie...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mushtaq, Muhammad-Sajid
Otros Autores: Mellouk, Abdelhamid
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : ISTEP Press Ltd., 2017.
Temas:
Multimedia communications.
R�eseaux multim�edias.
COMPUTERS > General.
Multimedia communications
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Machine generated contents note: ch. 1 Background and Contextual Study
  • 1.1. Introduction
  • 1.2. Subjective test
  • 1.2.1. Controlled environment approach
  • 1.2.2. Uncontrolled environment approach
  • 1.3. HTTP-based video streaming technologies
  • 1.3.1. Video streaming method
  • 1.3.2. Adaptive video delivery components
  • 1.4. HTTP-based adaptive video streaming methods
  • 1.4.1. Traditional streaming versus adaptive streaming
  • 1.4.2. Adobe's HTTP Dynamic Streaming (HDS)
  • 1.4.3. Microsoft Smooth Streaming (MSS)
  • 1.4.4. Apple's HTTP Live Streaming (HLS)
  • 1.4.5. MPEG's Dynamic Adaptive Streaming over HTTP (DASH)
  • 1.5. Scheduling and power-saving methods
  • 1.5.1. Scheduling methods
  • 1.5.2. DRX power-saving method'
  • ch. 2 Methodologies for Subjective Video Streaming QoE Assessment
  • 2.1. Introduction
  • 2.2. Metrics affecting the QoE
  • 2.2.1. Network parameters
  • 2.2.2. Video characteristics
  • 2.2.3. Terminal types
  • 2.2.4. Psychological factors
  • 2.3. Machine learning classification methods
  • 2.3.1. Naive Bayes
  • 2.3.2. Support vector machines
  • 2.3.3. K-nearest neighbors
  • 2.3.4. Decision tree
  • 2.3.5. Random forest
  • 2.3.6. Neural networks
  • 2.4. Experimental environment for QoE assessment
  • 2.4.1. Controlled environment approach
  • 2.4.2. Crowdsourcing environment approach
  • 2.5. Testbed experiment
  • 2.5.1. Experimental setup
  • 2.5.2. Data analysis using ML methods
  • 2.6. Analysis of users' profiles
  • 2.6.1. Case 1: interesting and non-interesting video contents
  • 2.6.2. Case 2: frequency, HD and non-HD video content
  • 2.7. Crowdsourcing method
  • 2.7.1. Crowdsourcing framework
  • 2.7.2. Framework architecture
  • 2.7.3. Firefox extension
  • 2.7.4. Java application
  • 2.8. Conclusion
  • ch. 3 Regulating QoE for Adaptive Video Streaming
  • 3.1. Introduction
  • 3.2. Adaptive streaming architecture
  • 3.3. Video encoding
  • 3.4. Client-server communication
  • 3.5. Rate-adaptive algorithm
  • 3.6. System model
  • 3.7. Proposed BBF method
  • 3.8. Experimental setup
  • 3.9. Results
  • 3.10. Conclusion
  • ch. 4 QoE-based Power Efficient LTE Downlink Scheduler
  • 4.1. Introduction
  • 4.2. overview of LTE
  • 4.3. E-model
  • 4.4. DRX mechanism
  • 4.5. Methodology and implementation
  • 4.5.1. Traditional algorithms
  • 4.5.2. Proposed QEPEM
  • 4.5.3. Scheduler architecture
  • 4.5.4. Scheduling algorithm
  • 4.6. Simulation setup
  • 4.7. Performance analysis with a fixed Deep Sleep duration of 20 ms
  • 4.8. Performance analysis with a fixed Light Sleep duration of 10 ms
  • 4.9. Conclusion
  • ch. 5 QoE and Power-saving Model for 5G Network
  • 5.1. Introduction
  • 5.2. QoE and 5G network
  • 5.2.1. Cloud-based future cellular network
  • 5.2.2. Traffic model
  • 5.2.3. QoE modeling and measurement
  • 5.3. Optimization models
  • 5.3.1. Network design
  • 5.3.2. QoE
  • 5.4. Results
  • 5.5. Power-saving mechanism for UE and VBS
  • 5.5.1. User equipment (UE)
  • 5.5.2. Base station
  • 5.6. Energy consumption model
  • 5.6.1. Virtual base station
  • 5.6.2. User equipment
  • 5.7. Results.

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