Delay-Rate-Distortion Optimization For Cloud-Based Collaborative Rendering

Cloud rendering is emerged as a new cloud service to satisfy user's desire for running sophisticated graphics applications on thin devices. However, traditional cloud rendering approaches, both remote rendering and local rendering, have limitations. Remote rendering shifts intensive rendering tasks to cloud server and streams rendered frames to client, which suffers from high delay and bandwidth usage. Local rendering sends graphics data to client and performs rendering on local devices, which requires initial buffering delay and demands high computation capacity at client. In this paper, we propose a novel cloud based collaborative rendering framework, which adaptively integrates remote rendering and local rendering. Based on the proposed framework, we study the delay-Rate-Distortion (d-R-D) optimization problem, in which the source rates are optimally allocated for streaming encoded video frames and graphics data to minimize the overall distortion under the bandwidth and response delay constraints. Experiment results demonstrate that the proposed collaborative rendering framework can effectively allocate source rates to achieve the minimal distortion compared to the traditional remote rendering and local rendering.