In this paper, we propose a new deep learning-based quality ranking framework to assist video list decoding methods in the context of unreliable video transmissions. The objective is to identify an intact image (corrected video frame) among a list of candidate images generated by a list decoding method, where all candidates, except for the intact image are corrupted. The framework comprises a deep learning-based no-reference image quality assessment (NR-IQA) for non-uniform video distortions (NUD) system to rank the candidate images according to their quality, which allows identifying the best one. To show the validity of our proposed framework, we develop an NR-IQA system relying on a proven patch-based convolutional neural network (CNN) architecture, which we adapt to better account for the non-uniform distortions observed in the candidate images, e.g., H.265 transmission errors during wireless communications. Specifically, we modify the patch size on which our CNN for non-uniform distortions (CNN-NUD) operates to capture a larger and more meaningful spatial context. Moreover, we develop a new training database using images resulting from various bit modifications in the received video packets, to simulate the list decoding process, and train the system using a full reference IQA (FR-IQA) method. Experiments on intra frames of videos encoded using H.265 show the ability of this system to identify an intact image among a set of five candidate images with an average accuracy of 96.6%, whereas traditional NR-IQA metrics or the initially trained CNN system offer poor accuracy ranging between 15.7% and 33.6%, respectively.