Increasing Player Performance and Game Experience in High Latency Systems

Cloud gaming services and remote play offer a wide range of advantages but can inherent a considerable delay between input and action also known as latency. Previous work indicates that deep learning algorithms such as artificial neural networks (ANN) are able to compensate for latency. As high latency in video games significantly reduces player performance and game experience, this work investigates if latency can be compensated using ANNs within a live first-person action game. We developed a 3D video game and coupled it with the prediction of an ANN. We trained our network on data of 24 participants who played the game in a first study. We evaluated our system in a second user study with 96 participants. To simulate latency in cloud game streaming services, we added 180 ms latency to the game by buffering user inputs. In the study we predicted latency values of 60 ms, 120 ms and 180 ms. Our results show that players achieve significantly higher scores, substantially more hits per shot and associate the game significantly stronger with a positive affect when supported by our ANN. This work illustrates that high latency systems, such as game streaming services, benefit from utilizing a predictive system

Network traffic characterisation, analysis, modelling and simulation for networked virtual environments

Networked virtual environment (NVE) refers to a distributed software system where a simulation, also known as virtual world, is shared over a data network between several users that can interact with each other and the simulation in real-time. NVE systems are omnipresent in the present globally interconnected world, from entertainment industry, where they are one of the foundations for many video games, to pervasive games that focus on e-learning, e-training or social studies. From this relevance derives the interest in better understanding the nature and internal dynamics of the network tra c that vertebrates these systems, useful in elds such as network infrastructure optimisation or the study of Quality of Service and Quality of Experience related to NVE-based services. The goal of the present work is to deepen into this understanding of NVE network tra c by helping to build network tra c models that accurately describe it and can be used as foundations for tools to assist in some of the research elds enumerated before. First contribution of the present work is a formal characterisation for NVE systems, which provides a tool to determine which systems can be considered as NVE. Based on this characterisation it has been possible to identify numerous systems, such as several video games, that qualify as NVE and have an important associated literature focused on network tra c analysis. The next contribution has been the study of this existing literature from a NVE perspective and the proposal of an analysis pipeline, a structured collection of processes and techniques to de ne microscale network models for NVE tra c. This analysis pipeline has been tested and validated against a study case focused on Open Wonderland (OWL), a framework to build NVE systems of di erent purpose. The analysis pipeline helped to de ned network models from experimental OWL tra c and assessed on their accuracy from a statistical perspective. The last contribution has been the design and implementation of simulation tools based on the above OWL models and the network simulation framework ns-3. The purpose of these simulations was to con rm the validity of the OWL models and the analysis pipeline, as well as providing potential tools to support studies related to NVE network tra c. As a result of this nal contribution, it has been proposed to exploit the parallelisation potential of these simulations through High Throughput Computing techniques and tools, aimed to coordinate massively parallel computing workloads over distributed resources