  The state-of-the-art neural video codecs have outperformed the most
sophisticated traditional codecs in terms of RD performance in certain cases.
However, utilizing them for practical applications is still challenging for two
major reasons. 1) Cross-platform computational errors resulting from floating
point operations can lead to inaccurate decoding of the bitstream. 2) The high
computational complexity of the encoding and decoding process poses a challenge
in achieving real-time performance. In this paper, we propose a real-time
cross-platform neural video codec, which is capable of efficiently decoding of
720P video bitstream from other encoding platforms on a consumer-grade GPU.
First, to solve the problem of inconsistency of codec caused by the uncertainty
of floating point calculations across platforms, we design a calibration
transmitting system to guarantee the consistent quantization of entropy
parameters between the encoding and decoding stages. The parameters that may
have transboundary quantization between encoding and decoding are identified in
the encoding stage, and their coordinates will be delivered by auxiliary
transmitted bitstream. By doing so, these inconsistent parameters can be
processed properly in the decoding stage. Furthermore, to reduce the bitrate of
the auxiliary bitstream, we rectify the distribution of entropy parameters
using a piecewise Gaussian constraint. Second, to match the computational
limitations on the decoding side for real-time video codec, we design a
lightweight model. A series of efficiency techniques enable our model to
achieve 25 FPS decoding speed on NVIDIA RTX 2080 GPU. Experimental results
demonstrate that our model can achieve real-time decoding of 720P videos while
encoding on another platform. Furthermore, the real-time model brings up to a
maximum of 24.2\% BD-rate improvement from the perspective of PSNR with the
anchor H.265.
