Total Power Minimization for Multiuser Video Communications Over CDMA Networks

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 P_t,i, the video coding bit rate R_s,i, and video encoder parameters that control the complexity and hence power consumption of the video coder (referred simply as complexity beta_i). Instead of determining P_t,i directly, we first determine the desired signal to interference-noise ratio (SINR) gamma_i. Based on the optimal gamma_i and R_s,i, we then determine P _t,i. Our analysis shows that the product of R_s,i and gamma_i is an important quantity. Given the complexity beta_i (i.e., given the compression power) and quality constraint, in order to reduce the transmission power, one should choose R_s,i and gamma_i to 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 R_s,i and gamma_i. 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 R_s,i and gamma_i that minimize R_s,itimesgamma_i for each video category and each possible beta_i while satisfying the quality constraint at user i. The second step searches the optimal {beta_i}_i=1,...,N for 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 {R_s,i,beta_i,gamma_i} are fixed regardless the channel conditions