Details

Autor(en) / Beteiligte

• Tang, Xinkun
• Xu, Ying
• Ouyang, Feng
• Zhu, Ligu
• Peng, Bo

Titel

A Cloud-Edge Collaborative Gaming Framework Using AI-Powered Foveated Rendering and Super Resolution

Ist Teil von

• International journal on semantic web and information systems, 2023-01, Vol.19 (1), p.1-19

Ort / Verlag

Hershey: IGI Global

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Cloud gaming (CG) has gradually gained popularity. By leveling shared computing resources on the cloud, CG technology allows those without expensive hardware to enjoy AAA games using a low-end device. However, the bandwidth requirement for streaming game video is high, which can cause backbone network congestion for large-scale deployment and expensive bandwidth bills. To address this challenge, the authors proposed an innovative edge-assisted computing architecture that collaboratively uses AI-powered foveated rendering (FR) and super-resolution (SR). Using FR, the cloud server can stream gaming video in lower resolution, significantly reducing the transmitted data volume. The edge server will then upscale the video using a game-specific SR model, recovering the quality of the video, especially for the areas players pay the most attention. The authors built a prototype system called FRSR and did thorough, objective comparative experiments to demonstrate that this architecture can reduce bandwidth usage by 39.47% compared with classic CG implementation for similar perceived quality.