# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Description** & **Recommended Threshold** \\ \hline Downlink server-client throughput & The required downlink throughput for six degrees-of-freedom (6 DoF) server-based networked VR & \textgreater{}[400–600] Mbps [9] \\ \hline Downlink server-client latency & The maximal downlink latency for 6 DoF server-based networked VR & \textless{}[5–20] ms [9] \\ \hline Client-to-client RTT & The allowed latency for effective user collaboration in VR. It is defined as the time needed for a client to update the server + the time needed for the server to update all other clients [11]. & \textless{}15 ms [11]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the recommended thresholds with respect to throughput, latency, and RTT for multi-user VR as derived from the literature. To fill the gap, this work aims to provide a thorough investigation of the effects of latency on the performance and perception of users of collaborative VR. Starting from the lessons learned from our previous work [12], an experimental framework for multi-user collaborative VR was designed and developed. This framework enables two simultaneous users to work together in a gamified, collaborative VR environment. In it, the two users are put in charge of baking a pizza in a virtual kitchen. To this end, a sequence of tasks (e.g., passing of objects, utilizing virtual tools, pressing buttons) must be completed collaboratively to successfully reach the final objective. This use case has been designed such that it incorporates the three main task types in collaborative VR, following the taxonomy of [PERSON] et al. [17]: deliberation, exploration, and manipulation. In addition, the framework allows for controlling different network-related parameters in the ongoing experience. For this work, we focused the analysis on latency (average and burst) due to its strong impact on interactivity. The impact analysis was performed by means of both objective as well as subjective measurements. On the one hand, the framework enables the identification of the most prominent network requirements to enable such a system and to objectively analyse the user performance. On the other hand, a user study is conducted evaluating the influence of uniform and burst latency on end-user Quality-of-Experience (QoE) (i.e., perception of latency, jerkiness, and de-synchronizations), performance and collaboration, subjective time perception, and the occurrence of cybersickness in a multi-user virtual environment. The results show that users are able to easily distinguish between distorted and non-distorted network configurations, but that making a distinction between different types of distortions is much less straightforward. This suggests that for multi-user VR systems too a perception threshold for both latency and burst seems to exist, which will need additional refinement through further experimentation. Furthermore, the user's role in the experience and its tasks, interactivity, and movement are shown to have an important influence on their subjective opinions. As such, gaining more in-depth understanding of the influence of these contextual factors would help VR designers and content providers to objectively and in (near) real-time monitor and counteract occasional drops in end-user QoE. The remainder of this paper is structured as follows: Section 2 provides an overview of relevant related research on (multi-user) VR systems. In Section 3, the architecture of the adopted system is discussed and the experimental methodology is presented. Section 4 gives an overview of the obtained results in terms of objective system performance and user testing results. Finally, Section 5 provides a brief overview of the main conclusions of this work.