# 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
Furthermore, it is also worth mentioning that the difference in perception between both user roles approaches significance ([PERSON] U, \(p=0.092\)) for scenario B (burst), with user A showing higher jerkiness perception (median = 4) than User B (median = 2). This could be explained by User A spending more time observing User B then the other way around, as User B had a more intense task schedule and often had to turn away from User A to operate the oven. One would expect a similar observation in scenario D (burst + latency), however, which was found not to be the case ([PERSON] U, \(p=0.458\)). In Figure 11c, indicating the perceived influence of jerkiness on collaboration, significant differences ([PERSON], \(p<0.001\)) were observed between scenario D (burst + latency) and scenarios A (no distortion) ([PERSON] post hoc, \(p<0.001\)) and B (burst) ([PERSON] post hoc, \(p<0.05\)). This is remarkable for scenario B (burst), which objectively speaking has the same amount of burst in the system as scenario D (burst + latency). As such, it seems that subjects grade the influence of given distortions based more on the accumulation of impairments (scenario D) rather than the actual amount of jerkiness in the system. Objectively speaking, this can be explained by the fact that the combination of burst and latency in scenario D can push latency up to 1000 ms half of the time, as was explained in Section 4.3. Subjectively, it is also possible that the presence of one impairment (latency) raises the awareness of other impairments in the system, making subjects more aware of jerkiness distortions in scenario D (burst + latency) than in scenario B (burst). This is in line with the observations regarding the perceived influence of jerkiness on object interaction (Figure 11b), in which a significant difference ([PERSON], \(p<0.05\)) between scenarios A (no distortions) and D (burst + latency) ([PERSON] post hoc, \(p<0.05\)) was observed. In addition, no statistical differences were observed between C (latency) and D (burst + latency), despite their objective difference in burst. _(3) Synchronization_: Figure 12 shows the extent to which participants perceived synchronization (either spatial or temporal) between their own view of the virtual environment and that of their collaborator, e.g., based on oral communication during collaboration (\"To what extent do you believe your collaborator and you had a synchronized view of the virtual environment?\"). Strongly significant differences ([PERSON], \(p<0.001\)) were observed between scenario D (burst + latency) and each of the other scenarios ([PERSON] post hoc, \(p<0.001\), \(p<0.01\) and \(p<0.05\) for A, B, and D, respectively). Therefore, it seems that mainly the combination of both latency and burst seems to amplify subjects' susceptibility to environment de-synchronizations between collaborators. Based on the weak significance between A (no distortions) and C (latency) ([PERSON] post hoc, \(p=0.064\)), one could argue that latency is the most prominent driving factor behind this observation. This makes sense, as latency is a distortion constantly present during interactions, allowing for easier observation of de-synchronizations, in contrast to the behavior of burst which catches up with reality within specific time frames. Figure 11: Overview of the perceived jerkiness 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 jerkiness. Significant differences are indicated as * (\(p<0.05\)), 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 \(=Q3-Q1\).