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We revisit the performance of a canonical system design for edge-assisted AR that simply combines off-the-shelf H.264 video encoding with a standard object tracking technique. Our experimental analysis shows that the simple canonical design for edge-assisted object detection can achieve within 3.07%/1.51% of the accuracy of ideal offloading (which assumes infinite network bandwidth and the total network transmission time of a single RTT) under LTE/5G mmWave networks. Our findings suggest that recent trend towards sophisticated system architecture design for edge-assisted AR appears unnecessary. We provide insights for why video compression plus on-device object tracking is so effective in edge-assisted object detection, draw implications to edge-assisted AR research, and pose open problems that warrant further investigation into this surprise finding. 

Edge-assisted Augmented Reality (AR) which offloads computeintensive Deep Neural Network (DNN)-based AR tasks to edge servers faces an important design challenge: how to pick the DNN model out of many choices proposed for each AR task for offloading. For each AR task, e.g., depth estimation, many DNN-based models have been proposed over time that vary in accuracy and complexity. In general, more accurate models are also more complex; they are larger and have longer inference time. Thus choosing a larger model in offloading can provide higher accuracy for the offloaded frames but also incur longer turnaround time, during which the AR app has to reuse the estimation result from the last offloaded frame, which can lead to lower average accuracy. In this paper, we experimentally study this design tradeoff using depth estimation as a case study. We design optimal offloading schedule and further consider the impact of numerous factors such as on-device fast tracking, frame downsizing and available network bandwidth. Our results show that for edge-assisted monocular depth estimation, with proper frame downsizing and fast tracking, compared to small models, the improved accuracy of large models can offset its longer turnaround time to provide higher average estimation accuracy across frames under both LTE and 5G mmWave. 

In this paper, we study how to support high-quality immer- sive multiplayer VR on commodity mobile devices. First, we perform a scaling experiment that shows simply replicating the prior-art 2-layer distributed VR rendering architecture to multiple players cannot support more than one player due to the linear increase in network bandwidth requirement. Second, we propose to exploit the similarity of background environment (BE) frames to reduce the bandwidth needed for prefetching BE frames from the server, by caching and reusing similar frames. We nd that there is often little sim- ilarly between the BE frames of even adjacent locations in the virtual world due to a “near-object” e ect. We propose a novel technique that splits the rendering of BE frames between the mobile device and the server that drastically enhances the similarity of the BE frames and reduces the network load from frame caching. Evaluation of our imple- mentation on top of Unity and Google Daydream shows our new VR framework, Coterie, reduces per-player network requirement by 10.6X-25.7X and easily supports 4 players for high-resolution VR apps on Pixel 2 over 802.11ac, with 60 FPS and under 16ms responsiveness.