TY - GEN
T1 - Virtual Reality Benchmark for Edge Caching Systems
AU - Alfares, Nader
AU - Kesidis, George
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - We introduce a Unity based benchmark for evaluating Virtual Reality (VR) delivery systems using edge-cloud caching. As VR applications and systems progress, the need to meet strict latency and Quality of Experience (QoE) requirements is increasingly evident. In the context of VR, traditional cloud architectures (e.g., remote AWS S3 for content delivery) often struggle to meet these demands, especially for users of the same application in different locations. With edge computing, resources are brought closer to users in efforts to reduce latency and improve QoEs. However, VR's dynamic nature, with changing fields of view (FoVs) and user synchronization requirements, creates various challenges for edge caching. We address the lack of suitable benchmarks and propose a framework that simulates multiuser VR scenarios while logging users' interaction with objects within their actual and predicted FoVs. The benchmark's activity log can then be played back through an edge cache to assess the resulting QoEs. This tool fills a gap by supporting research in the optimization of edge caching (and other edge-cloud functions) for VR streaming.
AB - We introduce a Unity based benchmark for evaluating Virtual Reality (VR) delivery systems using edge-cloud caching. As VR applications and systems progress, the need to meet strict latency and Quality of Experience (QoE) requirements is increasingly evident. In the context of VR, traditional cloud architectures (e.g., remote AWS S3 for content delivery) often struggle to meet these demands, especially for users of the same application in different locations. With edge computing, resources are brought closer to users in efforts to reduce latency and improve QoEs. However, VR's dynamic nature, with changing fields of view (FoVs) and user synchronization requirements, creates various challenges for edge caching. We address the lack of suitable benchmarks and propose a framework that simulates multiuser VR scenarios while logging users' interaction with objects within their actual and predicted FoVs. The benchmark's activity log can then be played back through an edge cache to assess the resulting QoEs. This tool fills a gap by supporting research in the optimization of edge caching (and other edge-cloud functions) for VR streaming.
UR - https://www.scopus.com/pages/publications/105005153400
UR - https://www.scopus.com/pages/publications/105005153400#tab=citedBy
U2 - 10.1109/VRW66409.2025.00274
DO - 10.1109/VRW66409.2025.00274
M3 - Conference contribution
AN - SCOPUS:105005153400
T3 - Proceedings - 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025
SP - 1256
EP - 1257
BT - Proceedings - 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025
Y2 - 8 March 2025 through 12 March 2025
ER -