  Remote communication has rapidly become a part of everyday life in both
professional and personal contexts. However, popular video conferencing
applications present limitations in terms of quality of communication,
immersion and social meaning. VR remote communication applications offer a
greater sense of co-presence and mutual sensing of emotions between remote
users. Previous research on these applications has shown that realistic point
cloud user reconstructions offer better immersion and communication as compared
to synthetic user avatars. However, photorealistic point clouds require a large
volume of data per frame and are challenging to transmit over bandwidth-limited
networks. Recent research has demonstrated significant improvements to
perceived quality by optimizing the usage of bandwidth based on the position
and orientation of the user's viewport with user-adaptive streaming. In this
work, we developed a real-time VR communication application with an adaptation
engine that features tiled user-adaptive streaming based on user behaviour. The
application also supports traditional network adaptive streaming. The
contribution of this work is to evaluate the impact of tiled user-adaptive
streaming on quality of communication, visual quality, system performance and
task completion in a functional live VR remote communication system. We perform
a subjective evaluation with 33 users to compare the different streaming
conditions with a neck exercise training task. As a baseline, we use
uncompressed streaming requiring ca. 300Mbps and our solution achieves similar
visual quality with tiled adaptive streaming at 14Mbps. We also demonstrate
statistically significant gains to the quality of interaction and improvements
to system performance and CPU consumption with tiled adaptive streaming as
compared to the more traditional network adaptive streaming.
