A Cross-Layer Approach for Maximizing Visual Entropy Using Closed-Loop Downlink MIMO

Abstract

We propose an adaptive video transmission scheme to achieve unequal error protection in a closed loop multiple input multiple output (MIMO) system for wavelet-based video coding. In this scheme, visual entropy is employed as a video quality metric in agreement with the human visual system (HVS), and the associated visual weight is used to obtain a set of optimal powers in the MIMO system for maximizing the visual quality of the reconstructed video. For ease of cross-layer optimization, the video sequence is divided into several streams, and the visual importance of each stream is quantified using the visual weight. Moreover, an adaptive load balance control, named equal termination scheduling (ETS), is proposed to improve the throughput of visually important data with higher priority. An optimal solution for power allocation is derived as a closed form using a Lagrangian relaxation method. In the simulation results, a highly improved visual quality is demonstrated in the reconstructed video via the cross-layer approach by means of visual entropy.