  Volumetric video based on Neural Radiance Field (NeRF) holds vast potential
for various 3D applications, but its substantial data volume poses significant
challenges for compression and transmission. Current NeRF compression lacks the
flexibility to adjust video quality and bitrate within a single model for
various network and device capacities. To address these issues, we propose HPC,
a novel hierarchical progressive volumetric video coding framework achieving
variable bitrate using a single model. Specifically, HPC introduces a
hierarchical representation with a multi-resolution residual radiance field to
reduce temporal redundancy in long-duration sequences while simultaneously
generating various levels of detail. Then, we propose an end-to-end progressive
learning approach with a multi-rate-distortion loss function to jointly
optimize both hierarchical representation and compression. Our HPC trained only
once can realize multiple compression levels, while the current methods need to
train multiple fixed-bitrate models for different rate-distortion (RD)
tradeoffs. Extensive experiments demonstrate that HPC achieves flexible quality
levels with variable bitrate by a single model and exhibits competitive RD
performance, even outperforming fixed-bitrate models across various datasets.
