With the overarching goal of developing user-centric Virtual Reality (VR) systems, a new wave of studies focused on understanding how users interact in VR environments has recently emerged. Despite the intense efforts, however, current literature still does not provide the right framework to fully interpret and predict users’ trajectories while navigating in VR scenes. This work advances the state-of-the-art on both the study of users’ behaviour in VR and the user-centric system design. In more detail, we complement current datasets by presenting a publicly available dataset that provides navigation trajectories acquired for heterogeneous omnidirectional videos and different viewing platforms—namely, head-mounted display, tablet, and laptop. We then present an exhaustive analysis on the collected data to better understand navigation in VR across users, content, and, for the first time, across viewing platforms. The novelty lies in the user-affinity metric, proposed in this work to investigate users’ similarities when navigating within the content. The analysis reveals useful insights on the effect of device and content on the navigation, which could be precious considerations from the system design perspective. As a case study of the importance of studying users’ behaviour when designing VR systems, we finally propose a user-centric server optimisation. We formulate an integer linear program that seeks the best stored set of omnidirectional content that minimises encoding and storage cost while maximising the user’s experience. This is posed while taking into account network dynamics, type of video content, and also user population interactivity. Experimental results prove that our solution outperforms common company recommendations in terms of experienced quality but also in terms of encoding and storage, achieving a savings up to 70%. More importantly, we highlight a strong correlation between the storage cost and the user-affinity metric, showing the impact of the latter in the system architecture design.

中文翻译：

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用户在 VR 中的行为是否相似？调查用户对系统设计的影响

随着开发以用户为中心的虚拟现实 (VR) 系统的总体目标，最近出现了新一波关注于了解用户在 VR 环境中如何交互的研究。然而，尽管付出了巨大的努力，但当前的文献仍然没有提供正确的框架来充分解释和预测用户在 VR 场景中导航时的轨迹。这项工作推进了研究用户在 VR 中的行为和以用户为中心的系统设计的最新技术。更详细地说，我们通过提供一个公开可用的数据集来补充当前数据集，该数据集提供为异构全向视频获取的导航轨迹和不同的观看平台——即头戴式显示器、平板电脑和笔记本电脑。然后，我们对收集的数据进行了详尽的分析，以更好地了解 VR 中跨用户、内容以及首次跨查看平台的导航。新颖之处在于用户亲和度指标，在这项工作中提出来调查用户在内容中导航时的相似性。该分析揭示了有关设备和内容对导航影响的有用见解，从系统设计的角度来看，这可能是宝贵的考虑因素。作为在设计 VR 系统时研究用户行为的重要性的案例研究，我们最终提出了以用户为中心的服务器优化。我们制定了一个整数线性程序，它寻求最佳存储的全向内容集，最大限度地减少编码和存储成本，同时最大限度地提高用户体验。这是在考虑网络动态、视频内容类型以及用户群交互性的同时提出的。实验结果证明，我们的解决方案在体验质量和编码和存储方面都优于一般公司建议，节省高达 70%。更重要的是，我们强调了存储成本和用户亲和度指标之间的强相关性，显示了后者在系统架构设计中的影响。实验结果证明，我们的解决方案在体验质量和编码和存储方面都优于一般公司建议，节省高达 70%。更重要的是，我们强调了存储成本和用户亲和度指标之间的强相关性，显示了后者在系统架构设计中的影响。实验结果证明，我们的解决方案在体验质量和编码和存储方面都优于一般公司建议，节省高达 70%。更重要的是，我们强调了存储成本和用户亲和度指标之间的强相关性，显示了后者在系统架构设计中的影响。