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Creators/Authors contains: "Akhtar, Anique"

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  1. ; ; ; ; (, 2024 IEEE International Conference on Image Processing (ICIP))
    Accepted Manuscript Full Text Available
  2. ; ; (, IEEE Transactions on Image Processing)
    Full Text Available 
  3. ; ; ; ; (, IEEE Transactions on Image Processing)
    Full Text Available 
  4. ; ; ; ; (, IEEE Transactions on Multimedia)
    null (Ed.)
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  5. ; ; ; ; ; (, IEEE Transactions on Multimedia)
    null (Ed.)
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  6. ; ; ; ; ; (, IEEE Visual Communication & Image Processing (VCIP) Conference)
    null (Ed.)
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  7. ; ; ; ; ; ; (, ICC 2019 - 2019 IEEE International Conference on Communications (ICC))
    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. 
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