Cloud-based game streaming is emerging as a convenient way to play games when clients have a good network connection. However, high-quality game streams need high bitrates and low latencies, a challenge when competing for network capacity with other flows. While some network aspects of cloud-based game streaming have been studied, missing are comparative performance and congestion responses to competing TCP flows. This paper presents results from experiments that measure how three popular commercial cloud-based game streaming systems - Google Stadia, NVidia GeForce Now, and Amazon Luna - respond and then recover to TCP Cubic and TCP BBR flows on a congested network link. Analysis of bitrates, loss rates and round-trip times show the three systems have markedly different responses to the arrival and departure of competing network traffic.

Computer games are often rendered with inconsistent frame timing (frame jitter), particularly in cloud-based game streaming where frames traverse network bottlenecks before being rendered. While previous studies have helped understand the Quality of Experience (QoE) with frame jitter, derived models have tended to be limited in their prediction ability for conditions not yet tested. This paper combines results from four different user studies that assess QoE based on frame jitter, the studies differing in games, game systems, and methods of induced frame time variation. Analysis of the results shows the degree to which frame jitter degrades QoE, and that playout interruption sizes matter while interrupt frequencies do not. The rich user study-based data set provides the basis for models for predicting game player QoE with frame jitter - models which should be predictive for both cloud-based game streaming and traditional games, and for a wide range of player actions and game genres.

The 13th ACM Multimedia Systems Conference (and associated workshops: MMVE 2022, NOSSDAV 2022, GameSys 2022) happened from 14th -- 17th June 2022 in Athlone, Ireland. The MMSys conference is an essential forum for researchers in multimedia systems to present and share their latest research findings in multimedia systems. After two years of online and hybrid editions, MMSys was held onsite in the beautiful Athlone. Besides the many high-quality technical talks spread across different multimedia areas and the wonderful keynote talks, there were a few events targeted especially at students, such as mentoring sessions and the doctoral symposium. The social events were significant this year since they were the first opportunity in two years for multimedia researchers to meet colleagues, collaborators, and friends and discuss the latest hot topics while sharing a pint of Guinness or a glass of wine. To encourage student authors to participate on-site, SIGMM has sponsored a group of students with Student Travel Grant Awards. Students who wanted to apply for this travel grant needed to submit an online form before the submission deadline. The selected students received either 1,000 or 2,000 USD to cover their airline tickets as well as accommodation costs for this event. Of the recipients, 11 were able to attend the conference. We asked them to share their unique experience attending MMSys'22. In this article, we share their reports of the event.

Cloud-based game streaming has the disadvantage of added latency from the thin client to the cloud-based server and back, decreasing player performance and degrading their experience. Attribute scaling can make the game eas-ier, potentially exactly counteracting the difficulty added by the latency. We incorporate attribute scaling models into two different games, deploy them on a commercial cloud-based game streaming system and evaluate their efficacy by measuring impact on player performance and Quality of Experience (QoE). Analysis through a user study shows that our compensation methods improve player performance and may improve QoE compared to no latency compensation.

Cloud-based game streaming has emerged as a viable way to play games anywhere with a good network connection. While previous research has studied the network turbulence of game streaming traffic, there is as of yet no work exploring how cloud-based game streaming responds to rival connections on a congested network. This paper presents experiments measuring and comparing the network response for three popular commercial streaming services - Google Stadia, NVidia GeForce Now, and Amazon Luna - competing with TCP flows on a congested network. Analysis of the bitrates, loss and latency show that the three systems have different adaptations to network congestion and vary in their fairness to competing TCP flows sharing a bottleneck link.

Cloud-based game streaming has emerged as a viable way to play games anywhere with a good network connection. While previous research has studied the network turbulence of game streaming traffic, there is as of yet no work exploring how cloud-base game streaming responds to rival connections on a congested network. This paper presents experiments measuring and comparing the network response for three popular commercial streaming services – Google Stadia, NVidia GeForce Now, and Amazon Luna – competing with TCP flows on a congested network. Analysis of the bitrates, loss and latency show that the three systems have marked different approaches to network congestion, but are mostly fair to competing TCP flows sharing a bottleneck link.

Computer games, one of the most popular forms of entertainment in the world, are increasingly online multiplayer, connecting geographically dispersed players in the same virtual world over a network. Network latency between players and the server can decrease responsiveness and increase inconsistency across players, degrading player performance and quality of experience. Latency compensation techniques are software-based solutions that seek to ameliorate the negative effects of network latency by manipulating player input and/or game states in response to network delays. We search, find, and survey more than 80 papers on latency compensation, organizing their latency compensation techniques into a novel taxonomy. Our hierarchical taxonomy has 11 base technique types organized into four main groups. Illustrative examples of each technique are provided, as well as demonstrated use of the techniques in commercial games.

Players of first-person shooter (FPS) games, such as Counter-strike: Global Offensive (CS: GO), seek low latencies in order to play well and have fun. Even network latencies as small as 10 milliseconds may decrease accuracy, score, and Quality of Experience (QoE), degredations that may be exacerbated for some weapons. This paper presents results from 40+ person user study that measures the impact of network latencies on players for the FPS game CS: GO. We setup a testbed where participants played 20+ rounds of CS: GO with controlled amounts of network latency with either a mid-range, rapid fire, high-precision weapon (an AK-47 assault rifle) or a close-range, slow fire, lower-precision weapon (a Nova shotgun). Analysis of the results shows even network latencies under 100 milliseconds degrade player performance (accuracy and score), avatar movements, and QoE, with the impact on player performance more pronounced for the assault rifle compared to the shotgun.

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.

While there have been network studies of traditional network games and streaming video, there is less work measuring cloud-based game streaming traffic and none on Google's Stadia. This paper presents experiments that provide a first look - measuring Stadia game traffic for several games, analyzing the bitrates, packet sizes and inter-packet times, and comparing the results to other applications. Results indicate Stadia, unlike traditional network game systems, rapidly sends large packets downstream and small packets upstream, similar to but still significantly different than video and at much higher rates than previous cloud-based game systems or video.