TY  - RPRT
A1  - Metzger, Florian
A1  - Schröder, Svenja
A1  - Rafetseder, Albert
T1  - Subjective And Objective Assessment Of Video Game Context Factors
N2  - The recently published ITU-T Recommendation G1.032 proposes   a list of factors that may influence cloud and online gaming Quality of Experience (QoE). This paper provides two practical evaluations of proposed system and context influence factors: First, it investigates through an online survey (n=488) the popularity of platforms, preferred ways of distribution, and motivational aspects including subjective valuations of characteristics offered by today's prevalent gaming platforms. Second, the paper evaluates a large dataset of objective metrics for various gaming platforms: game lists, playthrough lengths, prices, etc., and contrasts these metrics against the gamers' opinions. The combined data-driven approach presented in this paper complements in-person and lab studies usually employed.
KW  - Videospiel
KW  - Quality of Experience
KW  - Umfrage
KW  - video game QoE
KW  - video game context factors
KW  - online survey
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-242471
N1  - Originally written in 2018, but never published.
ER  - 
TY  - RPRT
A1  - Metzger, Florian
T1  - Crowdsensed QoE for the community - a concept to make QoE assessment accessible
N2  - In recent years several community testbeds as well as participatory sensing  platforms have successfully established themselves to provide open data to everyone interested. Each of them with a specific goal in mind, ranging from collecting radio coverage data up to environmental and radiation data. Such data can be used by the community in their decision making, whether to subscribe to a specific mobile phone service that provides good coverage in an area or in finding a sunny and warm region for the summer holidays.

However, the existing platforms are usually limiting themselves to directly measurable network QoS. If such a crowdsourced data set provides more in-depth derived measures, this would enable an even better decision making. A community-driven crowdsensing platform that derives spatial application-layer user experience from resource-friendly bandwidth estimates would be such a case, video streaming services come to mind as a prime example. In this paper we present a concept for such a system based on an initial prototype that eases the collection of data necessary to determine mobile-specific QoE at large scale. In addition we reason why the simple quality metric proposed here can hold its own.
KW  - Quality of Experience
KW  - Crowdsourcing
KW  - Crowdsensing
KW  - QoE
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203748
N1  - Originally written in 2017, but never published.
ER  -