Efficient and high-quality multimedia data collection is critical for urban sensing applications, ranging from traffic anomaly detection to environment monitoring. However, existing UAV-based and vehicle-enabled data collection schemes exhibit dual limitations: UAVs suffer from 5G long-haul transmission inefficiency and storage constraints forcing repetitive proximity flights for data offloading, while vehicular networks are plagued by spatial coverage gaps due to urban blind spots and untrusted device participation. To address these challenges, we propose a Trust-Aware Hierarchical Multimedia Data Collection (MBDC) framework that integrates UAVs and vehicles into a synergistic “UAV-vehicle-data center” pipeline, aimed at improving data collection efficiency. First, we design a storage-driven spatiotemporal coordination protocol, using vehicle mobility predictions to construct dynamic encounter windows and enabling UAVs to dynamically offload to optimal vehicles before storage thresholds are reached, thereby eliminating storage-induced interruptions. Second, to mitigate data loss risks caused by malicious vehicles, we introduce a UAV-assisted dynamic credibility assessment mechanism that evaluates vehicle reliability through active probe packets. Finally, we validate the MBDC framework using real-world urban mobility datasets (Beijing taxi trajectories), demonstrating its capability to minimize data collection cycle by 34.73%-58.57%.