Cloud rendering has been widely used as a new computing architecture for the industries of film, television and animation. However, it is different from traditional methods, such as the render farm and rental market, which can provide a variety of rendering software in the cloud to recede workloads based on cloud infrastructures. In general, task executions and resource operations of task scheduling are transparent to the user. This requires that the cloud rendering system should have the intelligent ability to perform the optimal resources scheduling and multi-terminal tasks management. Thus, the reliability of the cloud rendering system is a core research problem. To this end, the probabilistic model checking technology is employed to carry out the quantitative verification and performance evaluation of the cloud rendering process focusing on task scheduling. First, the rendering service failure will cause stochastic exceptions and instruction errors when cloud rendering is working, i.e., damaged files and task timeout. To this end, the DTMC-based probabilistic model is proposed to formalize the file preparation module, resource request module, and rendering task execution module. Second, considering QoS attributes, nine types of reliability property are introduced to quantitatively verify the cloud rendering system, based on which PCTL is used to describe the verification formula to execute the supporting tool PRISM. Finally, the feasibility and effectiveness of proposed method are demonstrated by case study and experiments, especially the performance of task scheduling can be guaranteed by system recovery and task switching according to the quantitative result generated from formal verifications. Therefore, the proposed method can improve the reliability of the cloud rendering system.