Convex Optimization
Adaptive mode- and diversity-control for video transmission on MIMO wireless channels
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
MIMO video broadcast via transmit-precoding and SNR-scalable video coding
IEEE Journal on Selected Areas in Communications
Optimal minimum distance-based precoder for MIMO spatial multiplexing systems
IEEE Transactions on Signal Processing
Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels
IEEE Transactions on Information Theory
Joint Source and Channel Coding for MIMO Systems: Is it Better to be Robust or Quick?
IEEE Transactions on Information Theory
An overview of limited feedback in wireless communication systems
IEEE Journal on Selected Areas in Communications
Optimal precoding for orthogonalized spatial multiplexing in closed-loop MIMO systems
IEEE Journal on Selected Areas in Communications
Overview of the Scalable Video Coding Extension of the H.264/AVC Standard
IEEE Transactions on Circuits and Systems for Video Technology
Scalable Joint Source-Channel Coding for the Scalable Extension of H.264/AVC
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper we investigate an optimal solution for adaptive H.264/SVC video transmission over Multiple-Input Multiple-Output (MIMO) channels. We first write the end-to-end distortion of the H.264/SVC video transmission over a diagonal MIMO channel. The total distortion is expressed following three physical layer parameters: power allocation, modulation spectral efficiency and Error Code Correction (ECC) code rate. Minimizing the total distortion is considered as an optimization problem containing both discrete and continuous variables. We use the Lagrangian method associated with Karush-Kuhn and Tucker conditions to find out the optimal continuous physical layer parameters. Concerting the discrete modulation spectral efficiency and ECC code rate, we exploit information of the MIMO system to remove all suboptimal configurations. Therefore, the optimal power allocation is computed only for a reduced number of discrete configurations. The performance of the proposed solution is evaluated over both statistical and realistic MIMO channels. Results show that the proposed solution performs an optimal resource allocation to achieve the best QoS regardless the channel conditions.