# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 4 Results and Discussion
### Subjective Results
#### 4.4.1 Perception of the Network  
(1) LatencyFigure 10 illustrates the subjective perception of latency in the system (\"Did you notice latency in the system?\") in terms of the obtained distributions on a 1-to-5 Likert scale. Figure 10a illustrates to what extent subjects perceived latency in each of the four scenarios. A Friedman test for ordinal data was performed between the four scenarios to reveal any significant differences in the data. As the test indicated significance (\(p<0.001\)), a pair-wise Dunn-Bonferroni post hoc test was applied. This showed significant differences between scenarios A (no distortions) and C (latency) (\(p<0.05\)) and between A and D (latency and burst) (\(p<0.001\)). Note that this also implies that no significant difference was observed in perceived latency between scenarios B (burst) and C (latency), which is remarkable as only scenario C effectively contains latency. One possible explanation is that subjects do not have sufficient knowledge and/or expertise (despite the pre-session explanation) to distinguish between different impairment types and their manifestations in VR, with confusion between latency and jerkiness (i.e., the visual representation of burst) as a result. A second explanation is that scenarios B (burst) and C (latency), despite their distortions, are still playable and immersive enough such that the distinctions between both types of impairments are often overlooked. As such, subjects do have an intuition about the system being impaired but cannot really put their finger on the exact cause of the observed distortions. Figure 10: Overview of the perceived latency in the system (**a**) and its assessed influence on object interaction (**b**) and collaboration (**c**), all on a 1-to-5 Likert scale, where a higher score indicates a more obvious presence/influence of latency. Significant differences are indicated as * (\(p<0.05\)), **(\(p<0.01\)) and *** (\(p<0.001\)), respectively, as obtained from Friedman tests followed by Dunn-Bonferroni post hoc tests. The full orange line indicates the median, the green dotted line the mean. The bullets indicate outliers, which are defined as samples laying outside the interval \([Q1-1.5\cdot IQR,Q3+1.5\cdot IQR]\), with IQR the Interquartile Range defined as \(IQR=Q3-Q1\). Figure 10b,c illustrate how participants assess the influence of the perceived latency on the interaction with virtual objects (\"To what extent did the latency (if any) interfere with the physical interaction with objects inside the virtual environment?\") and the collaboration with another participant (\"To what extent did the latency (if any) interfere with the physical interaction with your collaborator inside the virtual environment?\"), respectively. Here as well, Friedman tests were performed (both \(p<0.001\)), followed by pair-wise Dunn-Bonferroni post hoc tests. Once again, the same observation can be made with regard to scenarios B (burst) and C (latency). As such, it can be concluded that the perceived latency in the system and its influence on interactions in the system are in line with each other, independent of whether object or collaborator interactions are considered. _(2) Burst_: Figure 11 shows a similar analysis in terms of the perception of jerkiness (i.e., the visual manifestation of burst) in the system. As can be noticed from Figure 11a, only significant differences (Friedman, \(p<0.001\)) in perception were observed between configuration A (the non-distorted experience) and each of the distorted counterparts (post hoc Dunn-Bonferroni test, \(p<0.05\), \(p<0.05\), \(p<0.001\) with B, C and D, respectively.). No mutually significant differences were observed between the distorted scenarios B (burst), C (latency), and D (burst + latency). This is remarkable, as scenario C does not include any burst impairments in contrast to scenarios B and D. This again supports the hypothesis that users are able to detect distortions in the system but are unable to distinguish between actual distortion types.