In recent work we have introduced and evaluated a fair and dynamic joint Call Admission Control (CAC) and Multiple Access Control (MAC) framework, for Geostationary Orbit (GEO) Satellite Systems, named Fair Predictive Resource Reservation Access (FPRRA). The framework was based on accurate videoconference and data traffic prediction, made decisions after taking into account the provider's revenue, and was shown to be highly efficient. In this paper we enhance FPRRA by talking into account the users' satisfaction for making scheduling decisions and we focus on its evaluation in the absence of accurate MPEG-4 and H.264 video traffic modeling. In addition, we discuss the efficiency of our proposed scheme in comparison to other efficient schemes from the literature

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Satellite networks offer an efficient alternative where no terrestrial networks are available, and can offer a cost effective means of transferring data. Numerous proposals have addressed the problem of resource allocation in geostationary (GEO) satellites, and have been evaluated via various performance metrics related to user Quality of Service (QoS). However, ensuring that on average the users’ QoS requirements are satisfied does not guarantee the satisfaction of their individual Quality of Experience (QoE) requirements, especially in the case of bursty video users. This paper proposes the use of video scene identification and classification for traffic modeling at the scene level in order to improve resource allocation and user QoE in GEO satellite networks. The proposed call admission control and medium access control framework makes decisions based on available bandwidth, long-term and short-term user satisfaction and the revenue that the provider may gain.