The effects of jitter on the peceptual quality of video
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Video Acceptability and Frame Rate
IEEE MultiMedia
Subjective evaluation of packet service performance in UMTS and heterogeneous networks
Proceedings of the 2nd ACM international workshop on Quality of service & security for wireless and mobile networks
A Neural Network Based Test Bed for Evaluating the Quality of Video Streams in IP Networks
CERMA '06 Proceedings of the Electronics, Robotics and Automotive Mechanics Conference - Volume 01
Mobile Multimedia End-User Quality of Experience Modeling
ICDT '06 Proceedings of the international conference on Digital Telecommunications
Emerging Wireless Multimedia: Services and Technologies
Emerging Wireless Multimedia: Services and Technologies
Towards a Video QoE Definition in Converged Networks
ICDT '07 Proceedings of the Second International Conference on Digital Telecommunications
Addressing user expectations in mobile content delivery
Mobile Information Systems - Improving Quality of Service in Mobile Information Systems, Services and Networks
An ANFIS-Based Hybrid Video Quality Prediction Model for Video Streaming over Wireless Networks
NGMAST '08 Proceedings of the 2008 The Second International Conference on Next Generation Mobile Applications, Services, and Technologies
A basic multimedia quality model
IEEE Transactions on Multimedia
Perceived Audiovisual Quality of Low-Bitrate Multimedia Content
IEEE Transactions on Multimedia
A study of real-time packet video quality using random neural networks
IEEE Transactions on Circuits and Systems for Video Technology
Predicting quality of experience in multimedia streaming
Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia
Aplicação de técnicas e métodos de IHC para avaliar a QoE de serviços de TV interativa
Proceedings of the IX Symposium on Human Factors in Computing Systems
Quality of experience in distributed databases
Distributed and Parallel Databases
QoE model driven for network services
WWIC'10 Proceedings of the 8th international conference on Wired/Wireless Internet Communications
Vertical handover decision based on quality of experience in heterogeneous wireless networks
Proceedings of the 6th Euro American Conference on Telematics and Information Systems
The role of psychophysics laws in quality of experience assessment: a video streaming case study
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
A network management algorithm and protocol for improving QoE in mobile IPTV
Computer Communications
Quality of Experience Models for Multimedia Streaming
International Journal of Mobile Computing and Multimedia Communications
HybridNN: An accurate and scalable network location service based on the inframetric model
Future Generation Computer Systems
An autonomous QoE-driven network management framework
Journal of Visual Communication and Image Representation
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The streaming of multimedia contents (e.g., Mobile TV) is a bandwidth intensive service. The network operator's aim is to provide an acceptable user experience at minimal network resource usage. It is important from the network operator's perspective to be aware of: 1) the thresholds at which the user's perception of service quality becomes unacceptable; and 2) the degree of influence of each of the Quality of Service (QoS) parameters on the user perception. However, very little is known about the formal methods to optimize the use of QoS mechanisms in relation to the user's Quality of Experience (QoE). In this paper, we explain how the user's QoE can be captured. A statistical modelling technique is employed which, correlates QoS parameters with estimates of QoE perceptions and identifies the degree of influence of each QoS parameters on the user perception. The network operator can apply this information to efficiently, and accurately undertake network dimensioning and service provisioning strategies. This proposed methodology is applied to demonstrate QoE management strategies, thus paving the way towards QoE-aware QoS management.