Total Power Minimization for Multiuser Video Communications Over CDMA Networks
Xiaoan Lu, Yao Wang, Elza Erkip, D.J. Goodman · IEEE Transactions on Circuits and Systems for Video Technology · 2007
In this work, we consider a CDMA cell with multiple terminals transmitting video signals. We adapt the system parameters to minimize the sum of compression powers and transmitter powers of all users while guaranteeing the received video quality at each terminal. The adjustable parameters at user i include the transmitter power Pt,i, the video coding bit rate Rs,i, and video encoder parameters that control the complexity and hence power consumption of the video coder (referred simply as complexity betai). Instead of determining Pt,idirectly, we first determine the desired signal to interference-noise ratio (SINR) gammai. Based on the optimal gammaiand Rs,i, we then determine Pt,i. Our analysis shows that the product of Rs,iand gammaiis an important quantity. Given the complexity betai(i.e., given the compression power) and quality constraint, in order to reduce the transmission power, one should choose Rs,iand gammaito minimize their product. When only the total transmission power is concerned, the optimal operating points can be determined at individual users separately: each user should run the encoder to minimize the product of Rs,iand gammai. When the objective is to minimize the sum of compression and transmission powers of all users, the optimal solution can be found in two steps. The first step searches the optimal Rs,iand gammaithat minimize Rs,itimesgammaifor each video category and each possible betaiwhile satisfying the quality constraint at user i. The second step searches the optimal {betai}i=1,...,Nfor all users jointly, that minimizes the sum of transmission and compression powers of all users. The first step can be completed offline in advance, only the second step needs to be computed in real time based on channel conditions of the users. Our results indicate that for the same class of video users, the one who is closer to the base station compresses at a lower complexity. Simulation results show that significant power savings are obtained by our adaptive algorithms over nonadaptive approaches, where {Rs,i,betai,gammai} are fixed regardless the channel conditions