In recent years, multiprocessor system-on-chips (MPSoC) integrating CPU and GPU have been widely deployed in the fields of industrial control, automotive electronics, smart medical, etc. The open computing language (OpenCL) is regarded as a popular application programming standard for CPU-GPU MPSoC due to the power of fully exploiting the parallel computing power of GPU cores and the general-purpose computing power of CPU cores. However, during deploying OpenCL applications to CPU-GPU MPSoC, most of the existing research works have neglected the management of chip temperature and lifetime, resulting in the elevated peak temperature and the early occurrence of permanent failures. In this paper, we explore the lifetime-driven OpenCL application scheduling for latency minimization on CPU-GPU MPSoC under timing, temperature, energy consumption, and lifetime constraints. We propose a method composed of static and dynamic application scheduling techniques. The static application scheduling technique is built on the improved cross-entropy strategy with consideration of the OpenCL application characteristics in searching for optimal OpenCL application design points. The dynamic application scheduling technique is developed on the feedback control strategy capable of processing the new arrival applications for latency optimization at runtime. Experimental results show that our proposed method reduces the average delay of OpenCL applications by 34.58% while satisfying all design constraints.