  As shared, collaborative, networked, virtual environments become increasingly
popular, various challenges arise regarding the efficient transmission of model
and scene transformation data over the network. As user immersion and real-time
interactions heavily depend on VR stream synchronization, transmitting the
entire data sat does not seem a suitable approach, especially for sessions
involving a large number of users. Session recording is another
momentum-gaining feature of VR applications that also faces the same challenge.
The selection of a suitable data format can reduce the occupied volume, while
it may also allow effective replication of the VR session and optimized
post-processing for analytics and deep-learning algorithms. In this work, we
propose two algorithms that can be applied in the context of a networked
multiplayer VR session, to efficiently transmit the displacement and
orientation data from the users' hand-based VR HMDs. Moreover, we present a
novel method describing effective VR recording of the data exchanged in such a
session. Our algorithms, based on the use of dual-quaternions and multivectors,
impact the network consumption rate and are highly effective in scenarios
involving multiple users. By sending less data over the network and
interpolating the in-between frames locally, we manage to obtain better visual
results than current state-of-the-art methods. Lastly, we prove that, for
recording purposes, storing less data and interpolating them on-demand yields a
data set quantitatively close to the original one.
