Characterization of Multi-User Augmented Reality over Cellular Networks
Characterization of Multi-User Augmented Reality over Cellular Networks
复制标题
蜂窝网络上多用户增强现实的表征
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Yu Zhou
中科院分区：
文献类型：
--
作者：
Kittipat Apicharttrisorn;Bharath Balasubramanian;Jiasi Chen;R. Sivaraj;Yi;R. Jana;S. Krishnamurthy;Tuyen X. Tran;Yu Zhou
多个用户在同一物理空间内交互的增强现实（AR）应用越来越受欢迎（例如，Pokemon Go中的共享AR模式，Google的Just a Line中的虚拟涂鸦）。然而，在蜂窝网络上运行的多用户AR应用程序可能会经历非常高的端到端时延（在公共LTE网络上测量为12.5 s中位数）。为了描述和理解这个问题的根本原因，我们对两个流行的多用户AR应用的公共LTE和行业LTE测试平台进行了首次测量研究，得出了一些见解：（1）无线电接入网络（RAN）占端到端延迟的很大一部分（31.2%，或3.9秒中位数），导致AR用户在与现成AR应用程序中的一组常见虚拟对象交互时遇到高延迟;（2）AR网络流量的特征在于单个上行TCP连接上的大的间歇性尖峰，导致频繁的TCP慢启动，这会增加用户感知的延迟;（3）应用蜂窝运营商的通用流量管理机制QoS类标识符（QCI）可以帮助减少33%的AR延迟，但会影响非AR用户。基于这些见解，我们提出了网络感知和网络不可知的AR设计优化解决方案，以智能地适应IP数据包的大小，并定期提供上行链路数据可用性的信息，分别。我们的解决方案有助于提升网络性能，将端到端AR延迟和有效吞吐量提高约40- 70%。
Augmented reality (AR) apps where multiple users interact within the same physical space are gaining in popularity (e.g., shared AR mode in Pokemon Go, virtual graffiti in Google’s Just a Line). However, multi-user AR apps running over the cellular network can experience very high end-to-end latencies (measured at 12.5 s median on a public LTE network). To characterize and understand the root causes of this problem, we perform a first-of-its-kind measurement study on both public LTE and industry LTE testbed for two popular multi-user AR applications, yielding several insights: (1) The radio access network (RAN) accounts for a significant fraction of the end-to-end latency (31.2%, or 3.9 s median), resulting in AR users experiencing high, variable delays when interacting with a common set of virtual objects in off-the-shelf AR apps; (2) AR network traffic is characterized by large intermittent spikes on a single uplink TCP connection, resulting in frequent TCP slow starts that can increase user-perceived latency; (3) Applying a common traffic management mechanism of cellular operators, QoS Class Identifiers (QCI), can help by reducing AR latency by 33% but impacts non-AR users. Based on these insights, we propose network-aware and network-agnostic AR design optimization solutions to intelligently adapt IP packet sizes and periodically provide information on uplink data availability, respectively. Our solutions help ramp up network performance, improving the end-to-end AR latency and goodput by ~40-70%.
AVR：增强车辆现实
DOI：
10.1145/3210240.3210319
发表时间：
2018
期刊：
and Services
影响因子：
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作者：
Qiu, Hang;Ahmad, Fawad;Bai, Fan;Gruteser, Marco;Govindan, Ramesh
通讯作者：
Govindan, Ramesh