Translating English dialogue into dialectal Arabic depends on the target dialect, conversational context and speaker attributes, and these challenges are compounded by the scarcity of dialectal parallel data. We present our system for the AlexandriaX-2026 Context-Aware English-to-Dialectal Arabic Dialogue Translation constrained track, which restricts participants to the provided Alexandria dataset and models of ≤5B parameters. We fine-tune open LLMs, exploring different context composition strategies for training and inference, and find that mismatched strategies can outperform matched ones depending on the model. We further apply quality-aware decoding (QAD) via Minimum Bayes Risk, tuning the utility function and candidate pool size for consistent gains over greedy decoding. Our final system, a fine-tuned Gemma-3-4B-it with spBLEU-based QAD and a pool size of 50 samples, achieves 26.48 spBLEU and 42.3 chrF++, ranking third on the constrained-track leaderboard.