Code review is a fundamental process in software development that plays a pivotal role in ensuring code quality and reducing the likelihood of errors and bugs. However, code review can be complex, subjective, and time-consuming. \emph{Quality estimation}, \emph{comment generation}, and \emph{code refinement} constitute the three key tasks of this process, and their automation has traditionally been addressed separately in the literature using different approaches. In particular, recent efforts have focused on fine-tuning pre-trained language models to aid in code review tasks, with each task being considered in isolation. We believe that these tasks are interconnected, and their fine-tuning should consider this interconnection. In this paper, we introduce a novel deep-learning architecture, named \oapp, which employs cross-task knowledge distillation to address these tasks simultaneously. In our approach, we utilize a cascade of models to enhance both \emph{comment generation} and \emph{code refinement} models. The fine-tuning of the \emph{comment generation} model is guided by the \emph{code refinement} model, while the fine-tuning of the \emph{code refinement} model is guided by the \emph{quality estimation} model. We implement this guidance using two strategies: a feedback-based learning objective and an embedding alignment objective. We evaluate \oapp~by comparing it to state-of-the-art methods based on independent training and fine-tuning. Our results show that our approach generates better review comments, as measured by the \emph{BLEU} score, as well as more accurate \emph{code refinement} according to the \emph{CodeBLEU} score.