In this work, an online charging model is presented based on the IMS policy control and charging (PCC) framework, which takes into consideration user's agreements with the service provider (e.g., user loyalty programme, service subscription, etc.), with the possibility of providing online charging for services requiring a negotiable QoS in dynamically changing network conditions. The model aims to provide charging for services consisting of a number of media components, where the treatment of each media component within the charging system may differ based on the user's agreement with the service provider. For such services, the QoS negotiation process typically involves various stakeholders (user, network provider, service provider, etc.), and it results in a customised, user-specific combination of media components within the final service configuration. The existing IMS PCC architecture is not well-suited for charging services requiring a negotiable QoS. The proposed model builds upon and extends the online charging architecture specified in the IP multimedia subsystem (IMS) Release 8, and requires less signalling than the standard architecture. The proposed model is illustrated by using the imaginary adaptable movie stream service as a case study.
Abstract. Efficient management of scarce access network resources for growing volume of Machine-to-Machine (M2M) communications play an important role in a Long Term Evolution (LTE) network. Understanding the communication requirements of machine-to-machine (M2M) services, and linking them to technical, as well as economic aspects, is a crucial step towards "smarter" charging of such services. We discuss the capabilities of M2M services to postpone their communication in LTE's core network, called the Evolved Packet Core (EPC), to avoid times when usage of network resources would be expensive (e.g., while the network is congested). We introduce a context of a group of machines, which describes the postponement capabilities of M2M communication, which is used as an input to the online charging process. We illustrate the proposed approach and its benefits using a smart home M2M service as an example.
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