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Neural Radiance Field (NeRF) has emerged as a powerful technique for 3D scene representation due to its high rendering quality. Among its applications, mobile NeRF video-on-demand (VoD) is especially promising, beneting from both the scalability of the mobile devices and the immersive experience oered by NeRF. However, streaming NeRF videos over real-world networks presents signi cant challenges, particularly due to limited bandwidth and temporal dynamics. To address these challenges, we propose NeRFlow, a novel framework that enables adaptive streaming for NeRF videos through both bitrate and viewpoint adaptation. NeRFlow solves three fundamental problems: rst, it employs a rendering-adaptive pruning technique to determine voxel importance, selectively reducing data size without sacricing rendering quality. Second, it introduces a viewpoint-aware adaptation module that eciently compensates for uncovered regions in real time by combining preencoded master and sub-frames. Third, it incorporates a QoE-aware bitrate ladder generation framework, leveraging a genetic algorithm to optimize the number and conguration of bitrates while accounting for bandwidth dynamics and ABR algorithms. Through extensive experiments, NeRFlow is demonstrated to eectively improve user Quality of Experience (QoE) by 31.3% to 41.2%, making it an ecient solution for NeRF video streaming.