Voltage and Frequency Scaling (VFS) has been shown to reduce energy consumption effectively on system level. Most existing work in this field focused on deadline-constrained applications with finite schedule lengths. However, in typical real-time streaming applications, data processing is constantly activated by infinitely long data streams and operations on successive data instances are overlapped to achieve a tight throughput. This necessitates new VFS policies to perform energy efficient processing. In this thesis, we solve throughput-constrained VFS problems for real-time streaming applications with discrete frequency levels on a heterogeneous multi-processor platform. We propose discrete scaling algorithms for a multi-clock domains platform with local voltage switches per processor and for a single-clock domain platform with a global voltage switch for all processors. We prove NP-hardness for the local VFS problem and maximal open for the global VFS problem. A mixed integer linear program (MILP) is formulated for our local voltage scaling algorithm, while for its global counterpart, a three-stage heuristic incorporating MILP is proposed. Furthermore, two extensions of the proposed voltage scaling policies are presented to handle transition overheads and to include application level Time-Division Multiplexing (TDM) schedulers. Experiments show that for our modem application examples, the discrete local VFS algorithm achieves energy savings close to its continuous counterpart, and local voltage switching is much more beneficial in terms of energy saving than global voltage switching. For example, for our Wireless LAN (WLAN) application example, the continuous local VFS algorithm reduces energy by 29.62%, while the discrete local and global VFS algorithms reduce energy by 28.03% and 16.49%, respectively.