GameSys '21: Proceedings of the Workshop on Game Systems (GameSys '21)

GameSys '21: Proceedings of the Workshop on Game Systems (GameSys '21)

GameSys '21: Proceedings of the Workshop on Game Systems (GameSys '21)

Full Citation in the ACM Digital Library

L33t or N00b?: How Player Skill Alters the Effects of Network Latency on First Person Shooter Game

  • Shengmei Liu
  • Mark Claypool
  • Atsuo Kuwahara
  • James Scovell
  • Jamie Sherman

Game players generally want low network latency to maximize their chances of winning
-- in general, the lower the network latency, the less time between a player's action
and the intended outcome. But how much network latency affects players with different
levels of skill is not known. This paper presents results from a 36-person user study
that evaluates the impact of network latencies on Counter-strike: Global Offensive
(CS:GO), with skilled FPS game players divided into two groups -- one group with extensive
CS:GO experience and the other not. Analysis of the results shows that network latency
impacts higher-skill players more than lower-skill players, with higher-skill players
suffering greater score, accuracy and Quality of Experience degradations than do the
lower-skill players for the same network latency.

The Advertising in Free-to-play Games: A Game Theory Analysis

  • Yu Chen
  • Haihan Duan
  • Wei Cai

With the rapid market growth of free-to-play games, how to choose a proper revenue
model becomes an important problem for the game provider. The classical method is
the in-game purchase. To utilize the large install base of the free-to-play games,
numerous game providers have also adopted advertising. This paper analyzes the mixing
revenue model of the in-game purchase (premium subscription) and advertising. Taken
the player's snobbery into consideration, we prove the mixing revenue model existing
equilibrium in a two-stage Stackelberg model. The experimental result provides theoretical
support in the design of the revenue model of the free-to-play games.

Dissecting cloud game streaming platforms regarding the impacts of video encoding
and networking constraints on QoE

  • Franck Aumont
  • Frédérique Humbert
  • Christoph Neumann
  • Charles Salmon-Legagneur
  • Charline Taibi

Cloud gaming -- sometimes also referred to as game streaming -- is the concept of
executing games on remote servers -- typically within a cloud -- capturing the video
output normally displayed onto a screen and sending the result as a video stream to
end-user devices. While many game streaming solutions have existed for many years
now with first proof of concept shown in 2000, many of these solutions still seem
to suffer from technical or quality limitations. Recently, several new actors entered
the cloud gaming market with the promise to solve these limitations.

This paper provides a systematic benchmark of two game streaming services -- Google
Stadia and Nvidia GeForce Now -- under a variety of networking conditions, while subjectively
evaluating the resulting QoE. We further expose and analyse the network adaptation
and video encoding choices of these two platforms. We present a few key takeaways
and provide recommendations regarding video encoding choices, buffering and bitrate
adaptation mechanisms.

Towards Usable Attribute Scaling for Latency Compensation in Cloud-based Games

  • Edward Carlson
  • Tian Fan
  • Zijian Guan
  • Xiaokun Xu
  • Mark Claypool

Cloud-based games have advantages in convenience over traditional computer games,
but have the disadvantage of added latency from the thin client to the cloud-based
server and back. This added latency has been shown to decrease player performance.
New latency compensation techniques can help by scaling game attributes to make the
game easier, exactly counteracting the difficulty added by the latency. We conduct
a user study measuring attribute scaling for two games -- a first-person shooter and
a rhythm game -- each having a different attribute scaling method: spatial and temporal.
Data from the study shows a decrease in accuracy with an increase in latency and game
difficulty, and an increase in accuracy with an increase in attribute scaling. More
importantly, we derive a model from the data whereby a pre-determined accuracy can
be chosen -- say, by the game designer -- and the model then outputs the scaling factor
to meet that desired target accuracy.