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An IMS Based Mobile Podcasting Architecture Supporting Multicast/Broadcast Delivery

  • Rodolfo Cartas
  • Markus Kampmann
  • Heiko Perkuhn
  • Juan Miguel Espinosa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5310)

Abstract

Podcasting is an automatic content distribution mechanism that has gained popularity in the last couple of years. It differs from traditional vertical integrated media distribution such as Radio and Television in that 1) content is periodically made available over the Internet; and 2) media consumption takes places in computers or digital media players. This allows users to escape from the established schedule-based media experience and linear structure of broadcast programs. Content aquisition through podcasts can lead to scalability problems, when the number of subscribers grows quickly. The reason is that each client polls periodically for new content, usually about once per hour. It is expected that with the transformation of cell phones into portable information devices, this kind of services will be also used in mobile networks. This becomes a problem as bandwidth resources in these networks are scarce. In this paper we present an Advanced Podcasting Service that aims to reduce bandwidth usage maintaining an actual podcast subscription inside the network and reducing podcast distribution impact by selecting an appropriate delivery mechanism, such as multicast, while transparently supporting existing podcasting applications. To do so, the proposed architecture uses the IP Multimedia Subsystem (IMS) and the Multimedia Broadcast/Multicast Service (MBMS), both born from the efforts of the Third Generation Partnership Project (3GPP). Finally, the proposed architecture was implemented and evaluated in a local testbed.

Keywords

User Equipment Session Initiation Protocol Multicast Group Home Subscriber Server Really Simple Syndication 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rodolfo Cartas
    • 1
  • Markus Kampmann
    • 1
  • Heiko Perkuhn
    • 1
  • Juan Miguel Espinosa
    • 2
  1. 1.Ericsson ResearchHerzogenrathGermany
  2. 2.Communication and Distributed SystemsRWTH AachenAachenGermany

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