Distributed-fountain network code (DFNC) for content delivery in vehicular networks

Abstract

Vehicle-to-vehicle (V2V) communication utilizing dedicated short range communication (DSRC) has already been tested in field trials and is ready for potential deployment. This deployment would enable the possibility of large scale content delivery over small and large geographical areas. While content delivery in such vehicular networks can take advantage of the broadcast nature of DSRC, it has to cope with new challenges presented by dynamic topology, unpredictable erasures and lack of acknowledgements. Random linear network coding (RLNC) can address these challenges in theory, but the high decoding complexity limits its applicability in practice, especially for large scale content delivery. Motivated by this, a new network coding scheme for vehicular networks, distributed-fountain network code (DFNC), that has low encoding, re-encoding and decoding complexity is presented in this paper. DFNC uses a fountain code at the source and re-encoding at intermediate vehicles that approximate a fountain code. Re-encoding at intermediate vehicles comprises an innovative approach of low complexity degree reduction and random linear combination of degree-reduced packets to satisfy the degree distribution of the fountain code. Low-complexity belief propagation (BP) decoding is applied at the destinations. Through extensive simulations on two mobility models, random waypoint and Boston urban area, this paper establishes that DFNC performance is close to RLNC performance at order-wise lower complexity. DFNC significantly outperforms other relevant epidemic algorithms in literature.