  Nowadays, Internet of Video Things (IoVT) grows rapidly in terms of quantity
and computation demands. In spite of the higher local computation capability on
visual processing compared with conventional Internet of Things devices, IoVT
devices need to offload partial visual processing tasks to the mobile edge
computing (MEC) server wirelessly due to its larger computation demands.
However, visual processing task offloading is limited by uplink throughput and
computation capability of the MEC server. To break through these limitations, a
novel non-orthogonal multiple access (NOMA) assisted IoVT framework with
multiple MEC servers is proposed, where NOMA is exploited to improve uplink
throughput and MEC servers are co-located with base stations to provide enough
computation capability for offloading. In the proposed framework, the
association strategy, uplink visual data transmission assisted by NOMA and
division of the visual processing tasks as well as computation resource
allocation at the MEC servers are jointly optimized to minimize the total delay
of all visual processing tasks, while meeting the delay requirements of all
IoVT devices. Simulation results demonstrate that significant performance gains
can be achieved by proposed joint optimization with NOMA transmission and
multi-MEC offloading in the heterogeneous IoVT network.
