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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
ISTEP Press Ltd.,
2017.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Mushtaq, Muhammad-Sajid. | |
| 245 | 1 | 0 | |a Quality of experience paradigm in multimedia services : |b application to OTT video streaming and VoIP services / |c Muhammad-Sajid Mushtaq, Abdelhamid Mellouk. |
| 260 | |a London : |b ISTEP Press Ltd., |c 2017. | ||
| 300 | |a 1 online resource : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a 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 viewpoint of technical and non-technical parameters using multimedia services. It discusses the subjective methods for both controlled and uncontrolled environments. Collected datasets are used to analyze users' profiles, which sheds light on key factors to help network service providers understand end-users' behavior and expectations. Important adaptive video streaming technologies are discussed that run on unmanaged networks to achieve certain QoS features. The authors present a scheduling method to allocate resources to the end-user based on users' QoE and optimizes the power efficiency of users' device for LTE-A. Lastly, two key aspects of 5G networks are presented: QoE using multimedia services (VoIP and video), and power-saving model for mobile device and virtual base station. | ||
| 505 | 0 | 0 | |g Machine generated contents note: |g ch. 1 |t Background and Contextual Study -- |g 1.1. |t Introduction -- |g 1.2. |t Subjective test -- |g 1.2.1. |t Controlled environment approach -- |g 1.2.2. |t Uncontrolled environment approach -- |g 1.3. |t HTTP-based video streaming technologies -- |g 1.3.1. |t Video streaming method -- |g 1.3.2. |t Adaptive video delivery components -- |g 1.4. |t HTTP-based adaptive video streaming methods -- |g 1.4.1. |t Traditional streaming versus adaptive streaming -- |g 1.4.2. |t Adobe's HTTP Dynamic Streaming (HDS) -- |g 1.4.3. |t Microsoft Smooth Streaming (MSS) -- |g 1.4.4. |t Apple's HTTP Live Streaming (HLS) -- |g 1.4.5. |t MPEG's Dynamic Adaptive Streaming over HTTP (DASH) -- |g 1.5. |t Scheduling and power-saving methods -- |g 1.5.1. |t Scheduling methods -- |g 1.5.2. |t DRX power-saving method' -- |g ch. 2 |t Methodologies for Subjective Video Streaming QoE Assessment -- |g 2.1. |t Introduction -- |g 2.2. |t Metrics affecting the QoE -- |g 2.2.1. |t Network parameters -- |g 2.2.2. |t Video characteristics -- |g 2.2.3. |t Terminal types -- |g 2.2.4. |t Psychological factors -- |g 2.3. |t Machine learning classification methods -- |g 2.3.1. |t Naive Bayes -- |g 2.3.2. |t Support vector machines -- |g 2.3.3. |t K-nearest neighbors -- |g 2.3.4. |t Decision tree -- |g 2.3.5. |t Random forest -- |g 2.3.6. |t Neural networks -- |g 2.4. |t Experimental environment for QoE assessment -- |g 2.4.1. |t Controlled environment approach -- |g 2.4.2. |t Crowdsourcing environment approach -- |g 2.5. |t Testbed experiment -- |g 2.5.1. |t Experimental setup -- |g 2.5.2. |t Data analysis using ML methods -- |g 2.6. |t Analysis of users' profiles -- |g 2.6.1. |t Case 1: interesting and non-interesting video contents -- |g 2.6.2. |t Case 2: frequency, HD and non-HD video content -- |g 2.7. |t Crowdsourcing method -- |g 2.7.1. |t Crowdsourcing framework -- |g 2.7.2. |t Framework architecture -- |g 2.7.3. |t Firefox extension -- |g 2.7.4. |t Java application -- |g 2.8. |t Conclusion -- |g ch. 3 |t Regulating QoE for Adaptive Video Streaming -- |g 3.1. |t Introduction -- |g 3.2. |t Adaptive streaming architecture -- |g 3.3. |t Video encoding -- |g 3.4. |t Client-server communication -- |g 3.5. |t Rate-adaptive algorithm -- |g 3.6. |t System model -- |g 3.7. |t Proposed BBF method -- |g 3.8. |t Experimental setup -- |g 3.9. |t Results -- |g 3.10. |t Conclusion -- |g ch. 4 |t QoE-based Power Efficient LTE Downlink Scheduler -- |g 4.1. |t Introduction -- |g 4.2. |t overview of LTE -- |g 4.3. |t E-model -- |g 4.4. |t DRX mechanism -- |g 4.5. |t Methodology and implementation -- |g 4.5.1. |t Traditional algorithms -- |g 4.5.2. |t Proposed QEPEM -- |g 4.5.3. |t Scheduler architecture -- |g 4.5.4. |t Scheduling algorithm -- |g 4.6. |t Simulation setup -- |g 4.7. |t Performance analysis with a fixed Deep Sleep duration of 20 ms -- |g 4.8. |t Performance analysis with a fixed Light Sleep duration of 10 ms -- |g 4.9. |t Conclusion -- |g ch. 5 |t QoE and Power-saving Model for 5G Network -- |g 5.1. |t Introduction -- |g 5.2. |t QoE and 5G network -- |g 5.2.1. |t Cloud-based future cellular network -- |g 5.2.2. |t Traffic model -- |g 5.2.3. |t QoE modeling and measurement -- |g 5.3. |t Optimization models -- |g 5.3.1. |t Network design -- |g 5.3.2. |t QoE -- |g 5.4. |t Results -- |g 5.5. |t Power-saving mechanism for UE and VBS -- |g 5.5.1. |t User equipment (UE) -- |g 5.5.2. |t Base station -- |g 5.6. |t Energy consumption model -- |g 5.6.1. |t Virtual base station -- |g 5.6.2. |t User equipment -- |g 5.7. |t Results. |
| 650 | 0 | |a Multimedia communications. | |
| 650 | 6 | |a R�eseaux multim�edias. |0 (CaQQLa)201-0396065 | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Multimedia communications |2 fast |0 (OCoLC)fst01028918 | |
| 700 | 1 | |a Mellouk, Abdelhamid. | |
| 776 | 0 | 8 | |i Print version: |z 9781785481093 |z 1785481096 |w (OCoLC)950450411 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9781785481093 |z Texto completo |