Network Coding Channel Virtualization Schemes for Satellite Multicast Communications

In this paper, we propose two novel schemes to solve the problem of finding a quasi-optimal number of coded packets to multicast to a set of independent wireless receivers suffering different channel conditions. In particular, we propose two network channel virtualization schemes that allow for representing the set of intended receivers in a multicast group to be virtualized as one receiver. Such approach allows for a transmission scheme not only adapted to per-receiver channel variation over time, but to the network- virtualized channel representing all receivers in the multicast group. The first scheme capitalizes on a maximum erasure criterion introduced via the creation of a virtual worst per receiver per slot reference channel of the network. The second scheme capitalizes on a maximum completion time criterion by the use of the worst performing receiver channel as a virtual reference to the network. We apply such schemes to a GEO satellite scenario. We demonstrate the benefits of the proposed schemes comparing them to a per-receiver point-to-point adaptive strategy.

[1]  Samah A. M. Ghanem Network coding mechanisms for Ka-band satellite time varying channel , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[2]  Muriel Médard,et al.  XORs in the Air: Practical Wireless Network Coding , 2006, IEEE/ACM Transactions on Networking.

[3]  Don Towsley,et al.  Network Coding Performance for Reliable Multicast , 2007, MILCOM 2007 - IEEE Military Communications Conference.

[4]  Gökmen Altay Broadcast multicast capacity of network coding for random wireless networks , 2010, IET Commun..

[5]  Rudolf Ahlswede,et al.  Network information flow , 2000, IEEE Trans. Inf. Theory.

[6]  Shuo-Yen Robert Li,et al.  Linear network coding , 2003, IEEE Trans. Inf. Theory.

[7]  Mohamed-Slim Alouini,et al.  Completion time reduction in instantly decodable network coding through decoding delay control , 2014, 2014 IEEE Global Communications Conference.

[8]  Muriel Médard,et al.  A Theory of Network Equivalence— Part I: Point-to-Point Channels , 2011, IEEE Transactions on Information Theory.

[9]  Daniel Enrique Lucani,et al.  CORE: COPE with MORE in Wireless Meshed Networks , 2013, 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).

[10]  Sachin Katti,et al.  Trading structure for randomness in wireless opportunistic routing , 2007, SIGCOMM 2007.

[11]  Tracey Ho,et al.  A Random Linear Network Coding Approach to Multicast , 2006, IEEE Transactions on Information Theory.

[12]  Daniele Tarchi,et al.  Adaptive network coding schemes for satellite communications , 2016, 2016 8th Advanced Satellite Multimedia Systems Conference and the 14th Signal Processing for Space Communications Workshop (ASMS/SPSC).

[13]  Raymond W. Yeung Avalanche: A Network Coding Analysis , 2007, Commun. Inf. Syst..

[14]  Maria Angeles Vázquez-Castro,et al.  Statistical modeling of the LMS channel , 2001, IEEE Trans. Veh. Technol..

[15]  Baochun Li,et al.  R2: Random Push with Random Network Coding in Live Peer-to-Peer Streaming , 2007, IEEE Journal on Selected Areas in Communications.