Raptor codes have been proven very suitable for mobile broadcast and multicast

multimedia content delivery, and yet their computational complexity has not been in-

vestigated in the context of embedded systems. At the heart of Raptor codes are the

matrix inversion and vector decoder operations. This Thesis work at rst analyzes the

performance, energy pro le and resource implication of two matrix inversion and de-

coding algorithms; Gaussian elimination (GE) and 3rd Generation Partnership Group

(3GPP) Multimedia Broadcast/Multicast Services (MBMS) standard inactivation de-

coding Gaussian elimination (IDGE), for the Raptor decoder on a system on a chip

(SoC) platform with a soft-core embedded processor. The effect of the cache size, mem-

ory type and mapping on the performance of the two algorithms under consideration

are investigated. The work shows that with an appropriate data to memory mapping

formance compared with the software, requiring an energy consumption that is lower

bits). The Thesis work further shows that with parallel processing in hardware, using

be improved, while reducing the energy consumption. Extending the hardware word-