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Subjective assessment and instrumental prediction of mobile online gaming on the basis of perceptual dimensions

Motivation

The assessment of the perceived quality of the user (Quality of Experience) of pure audio and video material differs in many ways from the quality of computer games. The later possess a variety of factors due to their interactive nature. Not only factors of complex and innovative game systems have an impact on the QoE, but also the players themselves. A quality judgment, that results by comparing the expected and perceived composition of an entity, depends highly on the preferences, expectations and abilities of the player.

In this still young area of research standard methods for determining the QoE are not directly applicable. This is apparent since in a task oriented human-computer interaction the goal should be achieved with minimal effort. However in a game  the player exerts an effort in order to influence the outcome and thereby feels emotionally attached. Thus new concepts such as immersion and flow, a state of happiness while being in an equilibrium between competence and challenge, appear.

An analysis of the gaming market shows, that the proportion of mobile games has risen sharply in recent years. Mobile games are special in a sense that mobile devices such as smartphones or tablets were originally not designed for games and are therefore not optimally adapted to them. This also applies to the new concept of cloud gaming, where the entire game is executed on a server and only the video and audio material is transferred to the end user.

 

Aim of the project

The aim of this research project is to develop methods to assess the QoE of mobile games. In addition, based on a database containing subjective quality judgments, a model similar to the well known E-model should be constructed to predict the QoE. The following concrete steps are planned for this purpose:

  • Set up and modification of a testbed for conducting experiments including a cloud gaming system for mobile games
  • Development of a classification of games to choose representative games and identify system and user factors
  • Building a questionnaire covering a large space of relevant quality dimensions
  • Identification of quality-relevant perceptual dimensions and analysis of their impact on the overall quality
  • Analyzing the performance of current objective metrics which were proposed for different contents and services in mobile gaming
  • Building a QoE model based on game, system and network characteristics as well as user and context factors
Time Frame: 
01/2016 - 06/2019
T-labs Team Members:
Steven Schmidt
Funding by:
Deutsche Forschungsgemeinschaft (DFG)
Project Number:
MO 1038/21-1

List of Publications

Delay Sensitivity Classification of Cloud Gaming Content
Citation key sabet2020b
Author Sabet, Saeed Shafiee and Schmidt, Steven and Zadtootaghaj, Saman and Griwodz, Carsten and Möller, Sebastian
Title of Book Proceedings of the 12th ACM International Workshop on Immersive Mixed and Virtual Environment Systems
Pages 25–30
Year 2020
ISBN 9781450379472
DOI 10.1145/3386293.3397116
Location Istanbul, Turkey
Address New York, NY, USA
Month jun
Publisher Association for Computing Machinery
Series MMVE ’20
How Published Fullpaper
Abstract Cloud Gaming is an emerging service that catches growing interest in the research community as well as industry. Cloud Gaming require a highly reliable and low latency network to achieve a satisfying Quality of Experience (QoE) for its users. Using a cloud gaming service with high latency would harm the interaction of the user with the game, leading to a decrease in playing performance and, thus players frustrations. However, the negative effect of delay on gaming QoE depends strongly on the game content. At a certain level of delay, a slow-paced card game is typically not as delay sensitive as a shooting game. For optimal resource allocation and quality estimation, it is highly important for cloud providers, game developers, and network planners to consider the impact of the game content. This paper contributes to a better understanding of the delay impact on QoE for cloud gaming applications by identifying game characteristics influencing the delay perception of the users. In addition, an expert evaluation methodology to quantify these characteristics as well as a delay sensitivity classification based on a decision tree are presented. The results indicated an excellent level of agreement, which demonstrates the reliability of the proposed method. Additionally, the decision tree reached an accuracy of 90% on determining the delay sensitivity classes which were derived from a large dataset of subjective input quality ratings during a series of experiments.
Link to publication Link to original publication Download Bibtex entry

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