Title
Models for Capacity Demand Estimation in a TV Broadcast Network with Variable Bit Rate TV Channels
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.
Year
DOI
Venue
2008
10.1007/978-3-642-04576-9_1
Traffic Management and Traffic Engineering for the Future Internet
Keywords
Field
DocType
realistic mobile tv example,variable bit,tv broadcast system,capacity demand estimation,mobile tv,capacity gain,required transport capacity,rate tv channels,scalable video encoding,tv broadcast network,transport strategy,different quality,different version,constant bit rate,heterogeneous network,variable bit rate
Demand estimation,Computer science,Communication channel,Bit rate,Real-time computing,Heterogeneous network,Video Graphics Array,Broadcast television systems,Variable bitrate,Scalability
Conference
Volume
ISSN
Citations 
5464
0302-9743
0
PageRank 
References 
Authors
0.34
3
6
Name
Order
Citations
PageRank
Zlatka Avramova1274.24
Danny De Vleeschauwer242251.97
Kathleen Spaey36117.27
Sabine Wittevrongel421937.58
Herwig Bruneel5883116.07
Chris Blondia61249121.64