Enhancing Quality of Experience for Collaborative Virtual Reality with Commodity Mobile Devices
Enhancing Quality of Experience for Collaborative Virtual Reality with Commodity Mobile Devices
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DOI：
10.1109/icdcs54860.2022.00102
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Jiangong Chen;Feng Qian;Bin Li
中科院分区：
文献类型：
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作者：
Jiangong Chen;Feng Qian;Bin Li
虚拟现实（VR）与网络基础设施一起，可以同时为多个用户提供交互式和沉浸式体验，从而实现协作式 VR 应用（例如基于 VR 的教室）。然而，令人满意的用户体验不仅需要高分辨率的全景图像渲染，还需要极低的延迟和无缝的用户体验。此外，对有限网络资源的竞争（例如，多个用户共享有限的总带宽）对协作用户体验提出了重大挑战，特别是在容量时变的无线网络下。尽管现有的工作已经解决了其中一些挑战，但仍然缺乏考虑所有这些因素的原则性设计。在本文中，我们制定了一个组合优化问题来最大化体验质量（QoE），体验质量定义为质量、平均 VR 内容交付延迟和有限时间范围内质量方差的线性组合。特别是，在考虑感知内容的质量时，我们考虑了不完美运动预测的影响。然而，该问题的最优解决方案无法实时实施，因为它依赖于未来的决策。然后，我们将优化问题分解为每个时隙中的一系列组合优化，并开发出一种低复杂度算法，可以实现至少 1/2 的最优值。尽管如此，基于轨迹的模拟结果表明我们的算法的性能非常接近分解的最佳离线解决方案。此外，我们在商用移动设备的实际系统中实现了我们提出的算法，并证明了其优于最先进算法的性能。我们在 https://github.com/SNeC-Lab-PSU/ICDCS-CollaborativeVR 上开源了我们的实现。
Virtual Reality (VR), together with the network infrastructure, can provide an interactive and immersive experience for multiple users simultaneously and thus enables collaborative VR applications (e.g., VR-based classroom). However, the satisfactory user experience requires not only high-resolution panoramic image rendering but also extremely low latency and seamless user experience. Besides, the competition for limited network resources (e.g., multiple users share the total limited bandwidth) poses a significant challenge to collaborative user experience, in particular under the wireless network with time-varying capacities. While existing works have tackled some of these challenges, a principled design considering all those factors is still missing. In this paper, we formulate a combinatorial optimization problem to maximize the Quality of Experience (QoE), defined as the linear combination of the quality, the average VR content delivery delay, and variance of the quality over a finite time horizon. In particular, we incorporate the influence of imperfect motion prediction when considering the quality of the perceived contents. However, the optimal solution to this problem can not be implemented in real-time since it relies on future decisions. Then, we decompose the optimization problem into a series of combinatorial optimization in each time slot and develop a low-complexity algorithm that can achieve at least 1/2 of the optimal value. Despite this, the trace-based simulation results reveal that our algorithm performs very close to the decomposed optimal offline solution. Furthermore, we implement our proposed algorithm in a practical system with commercial mobile devices and demonstrate its superior performance over state-of-the-art algorithms. We open-source our implementations on https://github.com/SNeC-Lab-PSU/ICDCS-CollaborativeVR.