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Mobile edge and vehicle-based depth sending and real-time point cloud communication is an essential subtask enabling autonomous driving. In this paper, we propose a framework for point cloud multicast in VANETs using vehicle to infrastructure (V2I) communication. We employ a scalable Binary Tree embedded Quad Tree (BTQT) point cloud source encoder with bitrate elasticity to match with an adaptive random network coding (ARNC) to multicast different layers to the vehicles. The scalability of our BTQT encoded point cloud provides a trade-off in the received voxel size/quality vs channel condition whereas the ARNC helps maximize the throughput under a hard delay constraint. The solution is tested with the outdoor 3D point cloud dataset from MERL for autonomous driving. The users with good channel conditions receive a near lossless point cloud whereas users with bad channel conditions are still able to receive at least the base layer point cloud. 

Cognitive radio networks, a.k.a. dynamic spectrum access networks, offer a promising solution to the problems of spectrum scarcity and under-utilization. In this paper, we consider two single-user links: primary and secondary links. To increase secondary user (SU) transmission opportunities and increase primary user (PU) throughput, we consider a cognitive relay network where a SU relays PU packets that are unsuccessfully received at the primary receiver (PR). At the PR side, two protocols are suggested: i) energy accumulation (EA), and ii) mutual-information accumulation (MIA). The average stable throughput of the secondary link is derived under these protocols for a specific throughput selected by the primary link. Results show that EA and MIA can significantly improve the secondary throughput compared with the no accumulation scenario, especially under extreme environment.