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Models for Capacity Demand Estimation in a TV Broadcast Network with Variable Bit Rate TV Channels

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Traffic Management and Traffic Engineering for the Future Internet (FITraMEn 2008)

Abstract

Mobile TV is growing beyond the stage of experimentation and evaluation and is (about) to become part of our daily lives. Additionally, it is being delivered through heterogeneous networks and to a variety of receiving devices, which implies different versions of one and the same video content must be transported. We propose two (approximate) analytic methods for capacity demand estimation in a (mobile) TV broadcast system. In particular, the methods estimate the required transport capacity for a bouquet of channels offered on request and in different versions (video formats or in different quality) over a multicast-enabled network, encoded in non-constant bit rate targeting constant quality. We compare a transport strategy where the different versions (of one channel) are simulcast to a scalable video encoding (SVC) transport strategy, where all resolutions (of one channel) are embedded in one flow. In addition, we validate the proposed analytic methods with simulations. A realistic mobile TV example is considered with two transported resolutions of the channels: QVGA and VGA. We demonstrate that not always capacity gain is achieved with SVC as compared to simulcast since the former comes with some penalty rate and the gain depends on the system parameters.

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© 2009 Springer-Verlag Berlin Heidelberg

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Avramova, Z., De Vleeschauwer, D., Spaey, K., Wittevrongel, S., Bruneel, H., Blondia, C. (2009). Models for Capacity Demand Estimation in a TV Broadcast Network with Variable Bit Rate TV Channels. In: Valadas, R., Salvador, P. (eds) Traffic Management and Traffic Engineering for the Future Internet. FITraMEn 2008. Lecture Notes in Computer Science, vol 5464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04576-9_1

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  • DOI: https://doi.org/10.1007/978-3-642-04576-9_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04575-2

  • Online ISBN: 978-3-642-04576-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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