Image Broadcasting for Heterogeneous User Devices in MIMO Networks

This paper considers a multimedia broadcasting scenario in which two types of heterogeneous users with different display resolutions and different numbers of antennas stay in the service area. We propose an image broadcasting scheme that uses the image super-resolution (SR) techniques, spatial diversity, and diversity-multiplexing tradeoff (DMT) achieving codes. The proposed scheme broadcasts a low-resolution (LR) image to two types of users, along with residual pixel-error map containing high-frequency details of high-resolution (HR) image. Then, a user retaining an HR screen employs SR to reconstruct an HR image from the received LR image, and exploits the residual map to further enhance the image quality. Our scheme properly trains the neural network models of the deep learning-based SR by taking into account the source coding rates of the images. Considering the relationship between the number of antennas and screen resolution, based on hardware space of user devices, the proposed scheme encodes an LR image with spatial diversity, and encodes residual map with DMT-achieving codes. Numerical evaluation shows that our scheme significantly outperforms the baseline strategy that broadcasts either HR or LR images.