TY - GEN
T1 - Edge Cloud-based Augmented Reality
AU - Bachhuber, Christoph
AU - Martinez, Alvaro Sanchez
AU - Pries, Rastin
AU - Eger, Sebastian
AU - Steinbach, Eckehard
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - A convincing augmented reality (AR) experience requires vast computational resources, in particular for three-dimensional mapping of the environment, pose estimation and high-quality rendering of virtual objects. Even today's most powerful mobile devices can not provide such computational resources and consequently limit the achievable quality of the augmentation. To tackle this issue, all computations necessary for AR can be offloaded to the Edge Cloud, such that the mobile device merely acts as a camera and display. This approach introduces additional processing steps, namely video communication, which we carefully evaluate with respect to their influence on the quality of experience and energy consumption. In the evaluation of our prototype, we show that with a Glass-to-Glass delay of about 85 ms, our implementation is competitive against state-of-the-art solutions which run completely locally on a mobile device. Most notably, the additional steps required for offloading contribute little delay, which is often overcompensated by the faster computations in the Edge Cloud. A further benefit is that compared to performing all AR processing locally, offloading reduces the energy consumption in smartphones on average by 50 %. Moreover, the computational resources available for the AR application increase by a factor 10 to 100 through offloading. Finally, offloading enables high-quality AR applications even in low-end mobile devices.
AB - A convincing augmented reality (AR) experience requires vast computational resources, in particular for three-dimensional mapping of the environment, pose estimation and high-quality rendering of virtual objects. Even today's most powerful mobile devices can not provide such computational resources and consequently limit the achievable quality of the augmentation. To tackle this issue, all computations necessary for AR can be offloaded to the Edge Cloud, such that the mobile device merely acts as a camera and display. This approach introduces additional processing steps, namely video communication, which we carefully evaluate with respect to their influence on the quality of experience and energy consumption. In the evaluation of our prototype, we show that with a Glass-to-Glass delay of about 85 ms, our implementation is competitive against state-of-the-art solutions which run completely locally on a mobile device. Most notably, the additional steps required for offloading contribute little delay, which is often overcompensated by the faster computations in the Edge Cloud. A further benefit is that compared to performing all AR processing locally, offloading reduces the energy consumption in smartphones on average by 50 %. Moreover, the computational resources available for the AR application increase by a factor 10 to 100 through offloading. Finally, offloading enables high-quality AR applications even in low-end mobile devices.
KW - augmented reality
KW - edge cloud
KW - energy consumption
KW - glass-to-glass delay
UR - http://www.scopus.com/inward/record.url?scp=85075764442&partnerID=8YFLogxK
U2 - 10.1109/MMSP.2019.8901715
DO - 10.1109/MMSP.2019.8901715
M3 - Conference contribution
AN - SCOPUS:85075764442
T3 - IEEE 21st International Workshop on Multimedia Signal Processing, MMSP 2019
BT - IEEE 21st International Workshop on Multimedia Signal Processing, MMSP 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Workshop on Multimedia Signal Processing, MMSP 2019
Y2 - 27 September 2019 through 29 September 2019
ER -