Toward total quality of experience: A QoE model in a communication ecosystem

2012 ◽  
Vol 50 (4) ◽  
pp. 58-65 ◽  
Author(s):  
Khalil Ur Rehman Laghari ◽  
Kay Connelly
Keyword(s):  
Quality Of Experience ◽  
Total Quality ◽  
Qoe Model

Developing a Quality of Experience (QoE) model for Web Applications

2018 ◽  
Vol 29 (1) ◽  
pp. 175-199 ◽  
Author(s):  
Mark Lycett ◽  
Omar Radwan
Keyword(s):  
Quality Of Experience ◽  
Web Applications ◽  
Qoe Model

scholarly journals A Survey of Standardized Approaches towards the Quality of Experience Evaluation for Video Services: An ITU Perspective

2018 ◽  
Vol 2018 ◽  
pp. 1-25 ◽  
Author(s):  
Debajyoti Pal ◽  
Tuul Triyason
Keyword(s):  
Quality Of Experience ◽  
Video Quality ◽  
Multimedia Data ◽  
Future Research ◽  
The Internet ◽  
Sufficient Detail ◽  
Qoe Model ◽  
Audio Video ◽  
Work Done

Over the past few years there has been an exponential increase in the amount of multimedia data being streamed over the Internet. At the same time, we are also witnessing a change in the way quality of any particular service is interpreted, with more emphasis being given to the end-users. Thus, silently there has been a paradigm shift from the traditional Quality of Service approach (QoS) towards a Quality of Experience (QoE) model while evaluating the service quality. A lot of work that tries to evaluate the quality of audio, video, and multimedia services over the Internet has been done. At the same time, research is also going on trying to map the two different domains of quality metrics, i.e., the QoS and QoE domain. Apart from the work done by individual researchers, the International Telecommunications Union (ITU) has been quite active in this area of quality assessment. This is obvious from the large number of ITU standards that are available for different application types. The sheer variety of techniques being employed by ITU as well as other researchers sometimes tends to be too complex and diversified. Although there are survey papers that try to present the current state of the art methodologies for video quality evaluation, none has focused on the ITU perspective. In this work, we try to fill up this void by presenting up-to-date information on the different measurement methods that are currently being employed by ITU for a video streaming scenario. We highlight the outline of each method with sufficient detail and try to analyze the challenges being faced along with the direction of future research.

A Modeling Approach Based on Weighted LDA for Quality of Experience Measurement in Mobile Network Scenarios

2014 ◽  
Vol 571-572 ◽  
pp. 404-409
Author(s):  
Wei Kuang ◽  
Ling Han
Keyword(s):  
Quality Of Experience ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Service Providers ◽  
Mobile Network ◽  
Vector Model ◽  
Modeling Framework ◽  
Modeling Approach ◽  
Qoe Model

In this paper, we proposed a method for mining mobile users’ Quality of Experience (OoE) model based on weighted LDA. In the recent years, QoE has become an important concept for the quality of networks and services. At present , QoE has attracted the interest of network operators and service providers, because of providing a good QoE service to their customers can satisfy the customers and bring more users. In this paper, we are trying to build up users’ QoE model through topic model, an approach to generate a generative model for data mining. Latent Dirichlet Allocation (LDA) is a feasible and effective algorithm in text modeling. We propose an weighted LDA-based interest model within the modeling framework, and evaluate it on a mobile network users’ behavior extraction system. In this system, we can analyze the users’ behaviors, and build up a vector model for each user through a simple way. Besides, with the help of the topic model, we can get an exact model for users’ QoE, because we can generate the topic model through the vector model. Thus we can get the users’ QoE model, through which we can learn each user’s experience. Then the network operators can provide a better network service for their customers. In the end, we elaborate QoE management requirements for mobile network scenarios, and provide a QoE modeling approach for the mobile network scenarios.

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.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

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