Quality of Experience Factors for Mobile TV Users

2018 ◽  
pp. 473-496
Author(s):  
Dimitris N. Kanellopoulos
Keyword(s):  
Quality Of Experience ◽  
Mobile Tv ◽  
Road Map

This chapter helps the professionals involved in the Mobile TV industry to methodically engineer the Quality of Experience (QoE) of Mobile TV users. Its objective is to investigate the factors that influence the QoE of Mobile TV users. It also discusses the issues for strategic implications for the Mobile TV industry. We retrieved and categorized the majority of the critical works focusing on QoE for Mobile TV users. Then, we considered them and proposed a comprehensive road-map for improving the QoE of Mobile TV users. We present an approach to produce improvements to the Mobile TV customer experiences. This chapter proposes a seven-stage “road-map” to improvement, which develops the existing models. This study remains to be seen how the presented QoE factors– both amongst technologies and Mobile TV actors – will affect the potential for Mobile TV amongst various types of users. The proposed road-map can help to bridge gaps between other studies that have either focused on QoE for mobile TV or have addressed frameworks for mobile TV.

Quality of Experience Factors for Mobile TV Users

2016 ◽  
pp. 309-332
Author(s):  
Dimitris N. Kanellopoulos
Keyword(s):  
Quality Of Experience ◽  
Mobile Tv ◽  
Road Map

This chapter helps the professionals involved in the Mobile TV industry to methodically engineer the Quality of Experience (QoE) of Mobile TV users. Its objective is to investigate the factors that influence the QoE of Mobile TV users. It also discusses the issues for strategic implications for the Mobile TV industry. We retrieved and categorized the majority of the critical works focusing on QoE for Mobile TV users. Then, we considered them and proposed a comprehensive road-map for improving the QoE of Mobile TV users. We present an approach to produce improvements to the Mobile TV customer experiences. This chapter proposes a seven-stage “road-map” to improvement, which develops the existing models. This study remains to be seen how the presented QoE factors– both amongst technologies and Mobile TV actors – will affect the potential for Mobile TV amongst various types of users. The proposed road-map can help to bridge gaps between other studies that have either focused on QoE for mobile TV or have addressed frameworks for mobile TV.

Getting the Big Picture on Small Screens

2011 ◽  
pp. 31-46 ◽  
Author(s):  
Hendrik Knoche ◽  
M. Angela Sasse
Keyword(s):  
Quality Of Experience ◽  
Current Knowledge ◽  
Empirical Studies ◽  
Key Factors ◽  
Low Resolution ◽  
Mobile Tv ◽  
Technical Performance ◽  
Number Of Factors ◽  
Big Picture

This chapter provides an overview of the key factors that influence the quality of experience (QoE) of mobile TV services. It compiles the current knowledge from empirical studies and recommendations on four key requirements for the uptake of mobile TV services: (1) handset usability and its acceptance by the user, (2) the technical performance and reliability of the service, (3) the usability of the mobile TV service (depending on the delivery of content), and (4) the satisfaction with the content. It illustrates a number of factors that contribute to these requirements ranging from the context of use to the size of the display and the displayed content. The chapter highlights the interdependencies between these factors during the delivery of content in mobile TV services to a heterogeneous set of low resolution devices.

Getting the Big Picture on Small Screens

2011 ◽  
pp. 242-260 ◽  
Author(s):  
Hendrik Knoche ◽  
M. Angela Sasse
Keyword(s):  
Quality Of Experience ◽  
Current Knowledge ◽  
Empirical Studies ◽  
Key Factors ◽  
Low Resolution ◽  
Mobile Tv ◽  
Technical Performance ◽  
Number Of Factors ◽  
Big Picture

This chapter provides an overview of the key factors that influence the quality of experience (QoE) of mobile TV services. It compiles the current knowledge from empirical studies and recommendations on four key requirements for the uptake of mobile TV services: (1) handset usability and its acceptance by the user, (2) the technical performance and reliability of the service, (3) the usability of the mobile TV service (depending on the delivery of content), and (4) the satisfaction with the content. It illustrates a number of factors that contribute to these requirements ranging from the context of use to the size of the display and the displayed content. The chapter highlights the interdependencies between these factors during the delivery of content in mobile TV services to a heterogeneous set of low resolution devices.

Measuring Quality of Experience on a Commercial Mobile TV Platform

2010 ◽  
Author(s):  
Vlado Menkovski ◽  
Georgios Exarchakos ◽  
Antonio Liotta ◽  
Antonio Cuadra Sánchez
Keyword(s):  
Quality Of Experience ◽  
Mobile Tv

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.

scholarly journals Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sajeeb Saha ◽  
Md. Ahsan Habib ◽  
Tamal Adhikary ◽  
Md. Abdur Razzaque ◽  
Md. Mustafizur Rahman ◽  
...  
Keyword(s):  
Mobile Device ◽  
Quality Of Experience ◽  
Incentive Mechanism ◽  
Mobile Device Cloud

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

Sign in / Sign up

Export Citation Format

Share Document