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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yao Li Soljanin, E. |
| Copyright Year | 2009 |
| Description | Author affiliation: Bell Labs, Alcatel-Lucent, Murray Hill NJ 07974, USA (Soljanin, E.) || ECE Department, Rutgers University, Piscataway NJ 08854 (Yao Li) |
| Abstract | We investigate the performance of rateless codes for single-server streaming to diverse users, assuming that diversity in users is present not only because they have different channel conditions, but also because they demand different amounts of information and have different decoding capabilities. The LT encoding scheme is employed. While some users accept output symbols of all degrees and decode using belief propagation, others only collect degree-1 output symbols and run no decoding algorithm. We propose several performance measures, and optimize the performance of the rateless code used at the server through the design of the code degree distribution. Optimization problems are formulated for the asymptotic regime and solved as linear programming problems. Optimized performance shows great improvement in total bandwidth consumption over using the conventional ideal soliton distribution, or simply sending separately encoded streams to different types of user nodes. Simulation experiments confirm the usability of the optimization results obtained for the asymptotic regime as a guideline for finite-length code design. |
| Starting Page | 1419 |
| Ending Page | 1426 |
| File Size | 267304 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424458707 |
| e-ISBN | 9781424458714 |
| DOI | 10.1109/ALLERTON.2009.5394508 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-09-30 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | TV broadcasting Network servers Solitons Bandwidth Streaming media Decoding Multimedia communication Design optimization Guidelines Belief propagation |
| Content Type | Text |
| Resource Type | Article |
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