TY - GEN
T1 - RemoteVIO
T2 - 16th ACM Multimedia Systems Conference, MMSys 2025
AU - Jiang, Qinjun
AU - Pang, Yihan
AU - Sentosa, William
AU - Gao, Steven
AU - Huzaifa, Muhammad
AU - Zhang, Jeffrey
AU - Perez-Ramirez, Javier
AU - Das, Dibakar
AU - Gonzalez-Aguirre, David
AU - Godfrey, Brighten
AU - Adve, Sarita
N1 - We thank our shepherd Mario Montagud Climent and other anonymous reviewers for their valuable feedback. This work is supported in part by the National Science Foundation under grants 2120464 and 2217144, the IBM-Illinois Discovery Accelerator Institute (IIDAI), and gifts from Cisco and T-Mobile.
PY - 2025/3/31
Y1 - 2025/3/31
N2 - 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.
AB - 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.
KW - VR/AR
KW - extended reality
KW - head tracking
KW - low power
KW - offloading
KW - wireless networks
UR - http://www.scopus.com/inward/record.url?scp=105005026531&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=105005026531&partnerID=8YFLogxK
U2 - 10.1145/3712676.3714442
DO - 10.1145/3712676.3714442
M3 - Conference contribution
AN - SCOPUS:105005026531
T3 - MMSys 2025 - Proceedings of the 16th ACM Multimedia Systems Conference
SP - 101
EP - 112
BT - MMSys 2025 - Proceedings of the 16th ACM Multimedia Systems Conference
PB - Association for Computing Machinery
Y2 - 31 March 2025 through 3 April 2025
ER -