Adaptive Bitrate Streaming in Cloud Gaming

Cloud gaming streams games as video from a server to a client device making it susceptible to network congestion. Adaptive bitrate streaming estimates network capacity and sets encoding parameters to avoid exceeding the bandwidth of the connection. BBR is a congestion control algorithm as an alternative to current loss-based congestion control. We designed and implemented a bitrate adaptation heuristic based on BBR into GamingAnywhere, an open source cloud gaming platform. We conducted a user study and did objective analysis comparing our modified version to the original. Through our results, we found that our adaptive system was less challenging for players and improved retention rates and that there was no statistically significant difference in visual quality from objective testing

From One Edge to the Other:Exploring Gaming's Rising Presence on the Network

Audio and video streaming and on-demand services have dramatically changed how, and how much, media is consumed. Streaming generally allows a much larger selection of content, and arguably greater convenience. Gaming is the latest medium to place delivery of content into the cloud, via services such as Google Stadia and NVIDIA GeForce NOW. Just as with video streaming, this new ease comes with a hidden cost from the infrastructure used to deliver it, including the hardware cost, engineering cost and the energy to power the data centres and communications networks. Although gaming is currently only 7% of global network demand, with more than 95% of that being made up of downloading content, the possibility of streamed games could rapidly change this network footprint. In turn, this affects the yearly growth of energy impact from IT services. We explore possible futures where growth of these services continues, and we illustrate the implications a decade from now with three possible future scenarios for shifts of gaming practices. Our analyses show that game streaming will cause significant increases in the energy and carbon footprint of games

Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications

Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a three-dimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.Comment: Tp appear in the CCNC 2019 Conferenc