UAV-IoT for Next Generation Virtual Reality
UAV-IoT for Next Generation Virtual Reality
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DOI：
10.1109/tip.2019.2921869
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发表时间：
2019-12-01
影响因子：
10.6
通讯作者：
Chakareski, Jacob
中科院分区：
文献类型：
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作者：
Chakareski, Jacob
我们研究了用于远程场景虚拟现实（VR）沉浸体验的无人机 - 物联网数据采集与组网。我们将所实现的沉浸逼真度描述为所分配的无人机 - 物联网采集/网络速率的函数，并针对给定的系统/应用约束条件，研究使其最大化的优化问题。我们探索快速强化学习方法，以在感兴趣的场景中找到最佳的无人机 - 物联网动态网络布局，从而最大化预期的远程沉浸逼真度。我们设计可扩展的源 - 信道视点编码，以在地面聚合点处最大化在每个无人机位置采集的数据的预期重建逼真度。最后，我们探索分层定向组网以及速率 - 失真 - 功率优化的嵌入式调度方法，以有效地传输编码数据，并克服导致数据包缓冲的网络瞬态问题，这构成了我们框架的第四个系统组件。实验结果表明，与各自的最先进参考方法相比，我们框架的每个系统组件在实现的VR沉浸逼真度、应用交互性/播放延迟以及传输功耗方面，都显著提升了性能效率。
We investigate UAV-IoT data capture and networking for remote scene virtual reality (VR) immersion. We characterize the delivered immersion fidelity as a function of the assigned UAV-IoT capture/network rates and study the optimization problem of maximizing it, for given system/application constraints. We explore fast reinforcement learning to discover the best dynamic UAV-IoT network placement over the scene of interest to maximize the expected remote immersion fidelity. We design scalable source-channel viewpoint coding to maximize the expected reconstruction fidelity of the data captured at every UAV location at the ground-based aggregation point. Finally, we explore layered directional networking and rate-distortionpower optimized embedded scheduling methods to effectively transmit the encoded data and overcome network transients that lead to packet buffering, which represent the fourth system component of our framework. Experimental results demonstrate considerable performance efficiency gains enabled by each system component over the respective state-of-the-art reference methods, in delivered VR immersion fidelity, application interactivity/playout latency, and transmission power consumption.