A critical step in quasi-static ultrasound elastography is the estimation of time delay between two frames of radio-frequency (RF) data that are obtained while the tissue is undergoing deformation. This paper presents a novel technique for time-delay estimation (TDE) of all samples of RF data simultaneously, thereby exploiting all the information in RF data for TDE. A nonlinear cost function that incorporates similarity of RF data intensity and prior information of displacement continuity is formulated. Optimization of this function involves searching for TDE of all samples of the RF data, rendering the optimization intractable with conventional techniques given that the number of variables can be approximately one million. Therefore, the optimization problem is converted to a sparse linear system of equations, and is solved in real time using a computationally efficient optimization technique. We call our method GLobal Ultrasound Elastography (GLUE), and compare it to dynamic programming analytic minimization (DPAM) and normalized cross correlation (NCC) techniques. Our simulation results show that the contrast-to-noise ratio (CNR) values of the axial strain maps are 4.94 for NCC, 14.62 for DPAM, and 26.31 for GLUE. Our results on experimental data from tissue mimicking phantoms show that the CNR values of the axial strain maps are 1.07 for NCC, 16.01 for DPAM, and 18.21 for GLUE. Finally, our results on in vivo data show that the CNR values of the axial strain maps are 3.56 for DPAM and 13.20 for GLUE.