TY - GEN
T1 - Rendering Delay Minimization in RIS-Assisted Edge Computing for IIoT with VR Streaming
AU - Wang, Xuan
AU - Mukherjee, Mithun
AU - Kumar, V.
AU - Mavromoustakis, C. X.
AU - Shoufan, A.
AU - Guo, M.
AU - Zhang, Qi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The end-users who are subjected to inadequate wireless network coverage sometimes experience a lack of ef-ficient utilization of available bandwidth due to low channel gain. The circumstance mentioned above has a negative impact on the overall virtual reality (VR) streaming efficiency in the industrial Internet of Things (IIoT) with VR add-ons. This study focuses on examining a wireless network that utilizes a reconfigurable intelligent surface (RIS) to assist in improving the channel efficiency for end-user devices located at weak signal reception zones. The optimization problem formulation related to the allocation of rendering resources in edge servers and the distribution of downlink bandwidth for VR content from the edge server can be expressed as a quadratically constrained quadratic problem. The non-convex optimization problem has been solved by dividing the problem into three sub-problems and solved using the block coordinate descent (BCD) method. Based on our comprehensive simulation findings, we demonstrate a significant enhancement in reducing delays, including rendering and transmission delays, when employing RIS compared to other reference scenarios. This improvement is observed through an increase in the number of end-users, including those suffering from suboptimal signal reception conditions.
AB - The end-users who are subjected to inadequate wireless network coverage sometimes experience a lack of ef-ficient utilization of available bandwidth due to low channel gain. The circumstance mentioned above has a negative impact on the overall virtual reality (VR) streaming efficiency in the industrial Internet of Things (IIoT) with VR add-ons. This study focuses on examining a wireless network that utilizes a reconfigurable intelligent surface (RIS) to assist in improving the channel efficiency for end-user devices located at weak signal reception zones. The optimization problem formulation related to the allocation of rendering resources in edge servers and the distribution of downlink bandwidth for VR content from the edge server can be expressed as a quadratically constrained quadratic problem. The non-convex optimization problem has been solved by dividing the problem into three sub-problems and solved using the block coordinate descent (BCD) method. Based on our comprehensive simulation findings, we demonstrate a significant enhancement in reducing delays, including rendering and transmission delays, when employing RIS compared to other reference scenarios. This improvement is observed through an increase in the number of end-users, including those suffering from suboptimal signal reception conditions.
KW - AR/VR
KW - Edge computing
KW - IIoT
KW - reconfigurable intelligent surfaces
KW - rendering delay
UR - http://www.scopus.com/inward/record.url?scp=85190303600&partnerID=8YFLogxK
U2 - 10.1109/GCWkshps58843.2023.10465022
DO - 10.1109/GCWkshps58843.2023.10465022
M3 - Conference contribution
AN - SCOPUS:85190303600
T3 - 2023 IEEE Globecom Workshops, GC Wkshps 2023
SP - 1319
EP - 1324
BT - 2023 IEEE Globecom Workshops, GC Wkshps 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Globecom Workshops, GC Wkshps 2023
Y2 - 4 December 2023 through 8 December 2023
ER -