As mobile applications evolve rapidly, their fast iterative update nature leads to an increase in software defects. Just-In-Time Software Defect Prediction (JIT-SDP) offers immediate feedback on code changes. For new applications without historical data, researchers have proposed Cross-Project JIT-SDP (CP JIT-SDP). Existing CP JIT-SDP approaches are designed for offline scenarios where target data is available in advance. However, target data in real-world applications usually arrives online in a streaming manner, making online CP JIT-SDP face cross-project distribution differences and target project data concept drift challenges in online scenarios. These challenges often co-exist during application development, and their interactions cause model performance to degrade. To address these issues, we propose an online CP JIT-SDP framework called COTL. Specifically, COTL consists of two stages: offline and online. In the offline stage, the cross-domain structure preserving projection algorithm is used to reduce the cross-project distribution differences. In the online stage, target data arrives sequentially over time. By reducing the differences in marginal and conditional distributions between offline and online data for target project, concept drift is mitigated and classifier weights are updated online. Experimental results on 15 mobile application benchmark datasets show that COTL outperforms 13 benchmark methods on four performance metrics.