RemoteVIO: Offloading Head Tracking in an End-to-End XR System

Qinjun Jiang, Yihan Pang, William Sentosa, Steven Gao, Muhammad Huzaifa, Jeffrey Zhang, Javier Perez-Ramirez, Dibakar Das, David Gonzalez-Aguirre, Brighten Godfrey, Sarita V. Adve

Abstract: Power consumption, and the resulting limitation to computational load, is a first-order constraint in designing comfortable all-day-wear extended reality (XR) devices that can provide rich immersive experiences. This paper concerns reducing XR device power consumption by offloading head tracking, one of the top CPU and power consumers, to a remote server. We present RemoteVIO, the first open-source end-to-end XR system that offloads head tracking (visual inertial odometry or VIO) to a remote server. Our work distinguishes itself from past studies on computation offloading in XR by properly addressing two under-explored but critical aspects: 1) a comprehensive evaluation of user experience in a complete end-to-end XR system and 2) a quantification of the net power savings on real hardware.Through an Institutional Review Board (IRB) approved study, we find that RemoteVIO provides a satisfactory user experience under typical network conditions, but often degrades for network round trip time above 200 milliseconds (ms). We also demonstrate the first measured power savings from offloading head tracking on real hardware: compared with on-device tracking, RemoteVIO reduces CPU power by up to 52%, CPU+network power by up to 39%, and end-to-end full system power by up to 20%. Of equal importance, we examine the traditional approach of evaluating XR offloading techniques with datasets and quantitative metrics. Our results reveal that traditional head tracking metrics do not correlate with user experience, questioning the use of such metrics in XR systems research and underscoring the importance of using end-to-end systems that allow for user experience studies.

External IDs:dblp:conf/mmsys/JiangPSGH0PDGGA25