This paper addresses Quality of Experience (QoE)-driven, self-optimizing storage for distributed file sharing—a field gaining increasing attention in cloud and edge systems research. We present a novel platform for secure file sharing, centered on QoE-driven optimization of the Zettabyte File System (ZFS). The proposed four-module architecture integrates ZFS with reinforcement learning (RL) to dynamically tune QoE metrics such as latency, throughput, and caching efficiency, adapting to evolving workloads and user expectations. By leveraging RL, the system continuously optimizes ZFS configurations for enhanced performance. The four-layer architecture provides a coherent end-to-end framework that links user-level QoE signals to low-level ZFS tunables, while incorporating blockchain-based traceability to ensure transparency and trust. Experimental evaluations demonstrate that the adaptive deep Q-learning strategy improves storage performance and QoE compared to static configurations, establishing a new benchmark for QoE-driven decentralized storage.