Bandwidth Constrained Holographic Telepresence with 3D Model Reconstruction

With the emergence of virtual reality (VR) like holographic telepresence, it is envisaged that the current networks may be overwhelmed due to the higher bandwidth demand to support high-resolution videos. On the other hand, Camera Blending Field (CBF) has been proposed to restore the occluded regions of cameras to enhance the user’s Quality of Experience (QoE) in VR. In this paper, we explore the source selection to maximize the user’s QoE under the network capacity and 3D reconstruction constraints. We first formulate a new optimization problem, named Source Selection for Real-time Telepresence with Occlusion (SSRTO), with the objective of QoE maximization. Then, we prove the NP-hardness and propose a new algorithm, Maximum Quality of Complete Model (MQCM), to maximize the user’s QoE by examining the proximity of cameras on the built Angle Directed Graph (ADG) and Neighbor Directed Graph (NDG) for reconstructing the complete 3D model. Simulation results show that MQCM can effectively improve the user’s QoE by more than 100% compared with the baseline schemes.