Priority encoding transmission

Albanese, Andrés; Blömer, Johannes; Edmonds, Jeff; Luby, Michael; Sudan, Madhu

doi:10.1109/18.556670

Cited by 454 publications

(208 citation statements)

References 15 publications

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“…Another class of erasure codes that have recently been considered for network applications are Tornado codes, which have slightly worse erasure protecting properties, but can be encoded and decoded much more efficiently than RS codes [1]. In this work, we consider systematic RS codes, but the basic framework could easily be applied to other codes.…”

Section: Forward Error Correctionmentioning

confidence: 98%

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Joint source-channel coding and power adaptation for energy efficient wireless video communications

Zhai

Eisenberg

Pappas

et al. 2005

Signal Processing: Image Communication

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Section: Forward Error Correctionmentioning

confidence: 98%

“…1 The BER after channel decoding p b is a function of the BER before channel decoding, p e ; which is dependent on the state of the radio link, the specific channel code chosen, and the transmission power used. The details of the channel code are addressed in the next subsection.…”

Section: Channel Modelmentioning

confidence: 99%

Joint source-channel coding and power adaptation for energy efficient wireless video communications

Zhai

Eisenberg

Pappas

et al. 2005

Signal Processing: Image Communication

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“…We denote the corresponding weighted mean distortion by D MMA-α , which can be calculated using (2). Calculation of the optimal ratio of redundancy in the min-max-α sense is not more computation intensive than that of the CRA method.…”

Section: Min-max-α Rate Allocationmentioning

confidence: 99%

“…The sender can attempt to do this by maintaining an estimate of the channel state, i.e., the state of the transmission path from the sender to the receiver, and an estimate model of the perceived quality as a function of the redundancy rate and the channel state. Most of the work in the literature assumes a stationary channel with known parameters and aims at minimizing the mean distortion [2,13,24,29,34,35,44]. A few other works include feedback in the optimization problem, but consider the channel state estimate to be accurate and use it to minimize the mean distortion [1,5,33].…”

Section: Introductionmentioning

confidence: 99%

Robust source-channel coding for real-time multimedia

2007

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Multimedia applications operating in today's Internet have to employ some form of error resilience to cope with losses. For interactive applications with strict delay constraints the latency introduced by these schemes has to be low as well. Furthermore the parameters of the applied scheme have to be set based on measurements in a possibly rapidly changing environment. In this paper we propose a robust method, called min-max-α, for optimal source-channel code rate allocation to deal with time-varying packet channels and channel state estimation errors. We evaluate its performance when used with forward error correction and multiple description coding in both stationary and non-stationary environments. We show that on a stationary channel robust rate allocation is suboptimal in terms of mean distortion, but it achieves a lower variance, while on a non-stationary channel it prevents severe degradation of the quality. We apply the proposed rate allocation method to motion compensated video and show that it performs better on a non-stationary channel than minimization of the mean distortion proposed earlier.

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“…This allows us to have users with heterogeneous network connections, e.g., some users could have DSL connections and others could have T1 connections. The ability to handle heterogeneous users allows priority encoding transmission [2] or other means for users with higher bandwidth connections to get higher resolution broadcasts. Priority encoding transmission also allows graceful degradation of quality with network failures, as described in [6].…”

Section: Adversarial and Other Kinds Of Failuresmentioning

confidence: 99%

Building scalable and robust peer-to-peer overlay networks for broadcasting using network coding

2006

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We propose a scheme for building peer-topeer overlay networks for broadcasting using network coding. The scheme addresses many practical issues such as scalability, robustness, constraints on bandwidth, and locality of decisions. We analyze the system theoretically and prove near optimal bounds on the parameters defining robustness and scalability. As a result we show that the effects of failures are contained locally, allowing the network to grow exponentially with server load. We also argue that adversarial failures are no more harmful than random failures.

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Priority encoding transmission

Cited by 454 publications

References 15 publications

Joint source-channel coding and power adaptation for energy efficient wireless video communications

Joint source-channel coding and power adaptation for energy efficient wireless video communications

Robust source-channel coding for real-time multimedia

Building scalable and robust peer-to-peer overlay networks for broadcasting using network coding

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