Scalable video transmission over multiuser MIMO-OFDM systems

Abstract

With the proliferation of wireless services, multimedia interactivities are quickly becoming ubiquitous. As multimedia traffics generally have large packets volume, high data rate requirements in wireless transmission are critical. Next generation wireless systems, e.g., multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), provide high throughput and support flexible resource management strategies for multimedia services. By fully exploiting spatial, temporal, and frequency diversities of MIMO-OFDM systems, intelligent resource allocation schemes can increase the overall performance of the multimedia streaming system significantly. In this paper, we describe a general framework of cross-layer resource allocation design for scalable video transmission over multiuser MIMO-OFDM systems. Scalable video coding provides an efficient solution for video adaptation to satisfy diverse requirements from heterogeneous mobile clients according to their system specifications and channel conditions. Generally, the base layer of a scalable video bitstream is more important than the enhancement layer as the enhancement layer can only be used for decoding if the base layer is available. Scalable video packets are entitled with different priorities in video reconstructions. Adaptation can be achieved by discarding some enhancement layer packets when network is congested. Based on the characteristics of scalable video, our objective is to optimize the overall system performance for multiple scalable video downlink over the Space Division Multiple Access (SDMA)-OFDM system from a multiple-antenna base station. Our cross-layer optimization is achieved by jointly linking packet prioritization from application layer and radio resource allocation at the physical layer. Based on packet priority of scalable video, time-frequency resource, power and modulation schemes are adaptively selected based on a prioritized water filling algorithm to maximize the overall system performance and to ensure fairness among different users. The performance of the proposed strategy is demonstrated by experimental comparisons with conventional radio resource allocation schemes.