  Recent work on implicit neural representations (INRs) has evidenced their
potential for efficiently representing and encoding conventional video content.
In this paper we, for the first time, extend their application to immersive
(multi-view) videos, by proposing MV-HiNeRV, a new INR-based immersive video
codec. MV-HiNeRV is an enhanced version of a state-of-the-art INR-based video
codec, HiNeRV, which was developed for single-view video compression. We have
modified the model to learn a different group of feature grids for each view,
and share the learnt network parameters among all views. This enables the model
to effectively exploit the spatio-temporal and the inter-view redundancy that
exists within multi-view videos. The proposed codec was used to compress
multi-view texture and depth video sequences in the MPEG Immersive Video (MIV)
Common Test Conditions, and tested against the MIV Test model (TMIV) that uses
the VVenC video codec. The results demonstrate the superior performance of
MV-HiNeRV, with significant coding gains (up to 72.33\%) over TMIV. The
implementation of MV-HiNeRV is published for further development and
evaluation.
