Broadcasting is an efficient and scalable way of transmitting data over wireless channels to an unlimited number of clients. In this paper the problem of allocating data to multiple channels is studied, assuming flat data scheduling per channel and the presence of unrecoverable channel transmission errors. The objec- tive is that of minimizing the average expected delay experienced by the clients. Two different channel error models are considered: the Bernoulli model and the simplified Gilbert-Elliot one. In the former model, each packet transmission has the same probability to fail and each transmission error is independent from the oth- ers. In the latter one, bursts of erroneous or error-free packet transmissions due to wireless fading channels are modeled. For both channel error models, optimal solu- tions can be found in polynomial time when all data items have unit lengths, while heuristics are presented when data items have non-unit lengths. Extensive simula- tions, performed on benchmarks whose item popularities follow Zipf distributions, show that good sub-optimal solutions are found.
Data broadcasting algorithms on error-prone wireless channels
Barsocchi P.;Potorti' F.
2007
Abstract
Broadcasting is an efficient and scalable way of transmitting data over wireless channels to an unlimited number of clients. In this paper the problem of allocating data to multiple channels is studied, assuming flat data scheduling per channel and the presence of unrecoverable channel transmission errors. The objec- tive is that of minimizing the average expected delay experienced by the clients. Two different channel error models are considered: the Bernoulli model and the simplified Gilbert-Elliot one. In the former model, each packet transmission has the same probability to fail and each transmission error is independent from the oth- ers. In the latter one, bursts of erroneous or error-free packet transmissions due to wireless fading channels are modeled. For both channel error models, optimal solu- tions can be found in polynomial time when all data items have unit lengths, while heuristics are presented when data items have non-unit lengths. Extensive simula- tions, performed on benchmarks whose item popularities follow Zipf distributions, show that good sub-optimal solutions are found.| File | Dimensione | Formato | |
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