Dissecting Latency in 360° Video Camera Sensing Systems

360 degrees video camera sensing is an increasingly popular technology. Compared with traditional 2D video systems, it is challenging to ensure the viewing experience in 360 degrees video camera sensing because the massive omnidirectional data introduce adverse effects on start-up delay, event-to-eye delay, and frame rate. Therefore, understanding the time consumption of computing tasks in 360 degrees video camera sensing becomes the prerequisite to improving the system's delay performance and viewing experience. Despite the prior measurement studies on 360 degrees video systems, none of them delves into the system pipeline and dissects the latency at the task level. In this paper, we perform the first in-depth measurement study of task-level time consumption for 360 degrees video camera sensing. We start with identifying the subtle relationship between the three delay metrics and the time consumption breakdown across the system computing task. Next, we develop an open research prototype Zeus to characterize this relationship in various realistic usage scenarios. Our measurement of task-level time consumption demonstrates the importance of the camera CPU-GPU transfer and the server initialization, as well as the negligible effect of 360 degrees video stitching on the delay metrics. Finally, we compare Zeus with a commercial system to validate that our results are representative and can be used to improve today's 360 degrees video camera sensing systems.

Automated summary

The time consumption of tasks in 360 degrees video camera sensing is crucial for improving delay performance and viewing experience. This study provides an in-depth measurement of task-level time consumption and finds that camera CPU-GPU transfer and server initialization are key factors, while video stitching has a negligible impact on delay.