Advances in QoS/E Characterization and Prediction for Next Generation Mobile Communication Systems

Fuzzy Systems ◽  
2017 ◽  
pp. 1739-1765
Author(s):  
Charalampos N. Pitas ◽  
Apostolos G. Fertis ◽  
Dimitris E. Charilas ◽  
Athanasios D. Panagopoulos
Keyword(s):  
Quality Of Service ◽  
Communication Networks ◽  
Mobile Communication ◽  
Communication Systems ◽  
Quality Of Experience ◽  
Mobile Network ◽  
Mobile Users ◽  
Radio Access ◽  
Network Status

The scope of this work is to present a holistic approach in quality of service (QoS) and quality of experience (QoE) characterization and prediction in modern mobile communication networks. Analytically, multi radio access technologies have been deployed in order to deliver mobile services to quality demanded consumers. Quality of Experience (QoE) parameters describe the End-to-End (E2E) quality as experienced by the mobile users. These parameters are difficult to be measured and quantified. System Quality of Service (SQoS) parameters are metrics that are closely related to the network status, and defined from the viewpoint of the service provider rather than the service user. Moreover, E2E Service Quality of Service (ESQoS) parameters describe the QoS of the services and they are obtained directly from the QoE parameters by mapping them into parameters more relevant to network operators, service providers and mobile users. A useful technique for mobile network planning and optimization is to build reliable quality estimation models for mobile voice and video telephony service.

Advances in QoS/E Characterization and Prediction for Next Generation Mobile Communication Systems

2016 ◽  
pp. 512-535
Author(s):  
Charalampos N. Pitas ◽  
Apostolos G. Fertis ◽  
Dimitris E. Charilas ◽  
Athanasios D. Panagopoulos
Keyword(s):  
Quality Of Service ◽  
Communication Networks ◽  
Mobile Communication ◽  
Communication Systems ◽  
Quality Of Experience ◽  
Mobile Network ◽  
Mobile Users ◽  
Radio Access ◽  
Network Status

The scope of this work is to present a holistic approach in quality of service (QoS) and quality of experience (QoE) characterization and prediction in modern mobile communication networks. Analytically, multi radio access technologies have been deployed in order to deliver mobile services to quality demanded consumers. Quality of Experience (QoE) parameters describe the End-to-End (E2E) quality as experienced by the mobile users. These parameters are difficult to be measured and quantified. System Quality of Service (SQoS) parameters are metrics that are closely related to the network status, and defined from the viewpoint of the service provider rather than the service user. Moreover, E2E Service Quality of Service (ESQoS) parameters describe the QoS of the services and they are obtained directly from the QoE parameters by mapping them into parameters more relevant to network operators, service providers and mobile users. A useful technique for mobile network planning and optimization is to build reliable quality estimation models for mobile voice and video telephony service.

Quality of Experience (QoE) for Wireless Video Over Critical Communication Systems

2017 ◽  
pp. 132-167
Author(s):  
Emad Danish ◽  
Mazin I. Alshamrani
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Energy Consumption ◽  
Communication Systems ◽  
Quality Of Experience ◽  
Service Providers ◽  
Wireless Video ◽  
User Centric ◽  
Near Future

Video streaming is expected to acquire a massive share of the global internet traffic in the near future. Meanwhile, it is expected that most of the global traffic will be carried over wireless networks. This trend translates into considerable challenges for Service Providers (SP) in terms of maintaining consumers' Quality of Experience (QoE), energy consumption, utilisation of wireless resources, and profitability. However, the majority of Radio Resource Allocation (RRA) algorithms only consider enhancing Quality of Service (QoS) and network parameters. Since this approach may end up with unsatisfied customers in the future, it is essential to develop innovative RRA algorithms that adopt a user-centric approach based on users' QoE. This chapter focus on wireless video over Critical communication systems that are inspired by QoE perceived by end users. This chapter presents a background to introduce the reader to this area, followed by a review of the related up-to-date literature.

A Generic Adaptation Framework for Mobile Communication

2011 ◽  
Vol 2 (1) ◽  
pp. 46-57
Author(s):  
Hong Sun ◽  
Ning Gui ◽  
Chris Blondia
Keyword(s):  
Quality Of Service ◽  
Mobile Devices ◽  
Mobile Communication ◽  
Quality Of Experience ◽  
Adaptation Strategies ◽  
Easy Access ◽  
Mobile Terminals ◽  
System Resources ◽  
New Applications

Today, technologies are providing mobile terminals with much more powerful computational abilities. Such improvement has made it possible to run many complex applications on mobile devices. However, many of these new applications are also resource demanding. Lacking sufficient resources would cause performance failures and impact negatively on the users’ quality of experience. In order to improve this, it is important to provide the users with an easy access to specifying their requirements. It is also crucial to monitor the system resources and make corresponding adaptation immediately according to the user’s specifications. In this paper, the authors propose adaptation strategies that flexibly combine the process of monitoring and adaptation, which provides an easy way to specify user’s requirements. By tuning the quality of service, the applications’ demand on system resources is reduced, thus decreasing the chances of performance failures and improving the users’ quality of experience.

A Generic Adaptation Framework for Mobile Communication

2012 ◽  
pp. 196-207
Author(s):  
Hong Sun ◽  
Ning Gui ◽  
Chris Blondia
Keyword(s):  
Quality Of Service ◽  
Mobile Devices ◽  
Mobile Communication ◽  
Quality Of Experience ◽  
Adaptation Strategies ◽  
Easy Access ◽  
Mobile Terminals ◽  
System Resources ◽  
New Applications

Today, technologies are providing mobile terminals with much more powerful computational abilities. Such improvement has made it possible to run many complex applications on mobile devices. However, many of these new applications are also resource demanding. Lacking sufficient resources would cause performance failures and impact negatively on the users’ quality of experience. In order to improve this, it is important to provide the users with an easy access to specifying their requirements. It is also crucial to monitor the system resources and make corresponding adaptation immediately according to the user’s specifications. In this paper, the authors propose adaptation strategies that flexibly combine the process of monitoring and adaptation, which provides an easy way to specify user’s requirements. By tuning the quality of service, the applications’ demand on system resources is reduced, thus decreasing the chances of performance failures and improving the users’ quality of experience.

scholarly journals A Quality of Experience Management Framework for Mobile Users

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
María Jesús Algar ◽  
Isaac Martín de Diego ◽  
Alberto Fernández-Isabel ◽  
Miguel Ángel Monjas ◽  
Felipe Ortega ◽  
...  
Keyword(s):  
Communication Systems ◽  
Quality Of Experience ◽  
Automated System ◽  
Mobile Users ◽  
Voice Transmission ◽  
Real Time Analysis ◽  
Mitigation Action ◽  
Video Traces ◽  
Qoe Model

Voice transmission is no longer the main usage of mobile phones. Data transmissions, in particular Internet access, are very common actions that we might perform with these devices. However, the spectacular growth of the mobile data demand in 5G mobile communication systems leads to a reduction of the resources assigned to each device. Therefore, to avoid situations in which the Quality of Experience (QoE) would be negatively affected, an automated system for degradation detection of video streaming is proposed. This approach is named QoE Management for Mobile Users (QoEMU). QoEMU is composed of several modules to perform a real-time analysis of the network traffic, select a mitigation action according to the information of the traffic and to some predefined policies, and apply these actions. In order to perform such tasks, the best Key Performance Indicators (KPIs) for a given set of video traces are selected. A QoE Model is trained to define a global QoE for the set of traces. When an alert regarding degradation in the quality appears, a proper mitigation plan is activated to mitigate this situation. The performance of QoEMU has been evaluated over a degradation situation experiments with different video users.

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