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In this work we address reliable file delivery over mobile broadcast networks, concentrating on the Raptor codes as specified for Multimedia Broadcast/Multicast Services (MBMS) within 3GPP. We start by describing Luby-Transform (LT) codes, which are the first practical fountain codes. Then, using a natural and easy to understand linear algebra notation, we describe Raptor codes as a powerful extension of LT codes. We provide some insight into the Raptor code structure and some guidelines for implementation of encoders and decoders. Finally, some selected simulations verify the good performance of file distribution with Raptor codes as specified in 3GPP. References to a complete set of simulation results are also provided.

Recently, Multimedia Broadcast Multicast Service (MBMS) has been specified by 3GPP as a Release 6 feature in order to meet the increasing demands of multimedia download and streaming applications in mobile scenarios. H.264, as the unique recommended video codec for MBMS, serves as an essential component because of its high compression efficiency and easy network integration capability. In this study, we introduce and analyze the main system design parameters that influence the performance of the H.264 encoded video streaming over EGPRS and UMTS bearers. Effective design methodology including robustness against packet losses and efficient use of the scarce radio resources is presented. Care is taken on the processing power of mobiles, service delay constraints, and heterogeneous receiving conditions. Then, we investigate application of an advanced receiver concept, the so-called permeable layer receiver, in MBMS video broadcasting environments. Selected simulation results show the suitability of certain parameter selection as well as the benefits provided by the advanced receiver concept. Finally, a real-time test bed for MBMS called RealNeS-MBMS is presented. With this tool, a standard-compliant GERAN network can be simulated and the system design procedure including H.264 based video broadcast streaming can be evaluated in real-time.

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