Delay-Optimal Burst Erasure Code Construction

Martinian, E.; Trott, M.D.

doi:10.1109/isit.2007.4557355

Cited by 79 publications

(75 citation statements)

References 3 publications

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“…Streaming burst-erasure codes developed in [1] and [2] are single user codes for the model in the previous section. They correct an erasure burst of length B with a delay of T symbols and achieve the largest possible rate…”

Section: Background: Streaming Codes (Sco)mentioning

confidence: 99%

“…5 Since the diagonals b [2]) and b [2], e [3]) involve two or fewer erasures, we can now recover these sub-symbols using parity checks of code C 1 recovered in the previous step. In particular, the upper-right triangle source subsymbols a…”

Section: Examplementioning

confidence: 99%

“…Note that b [2], q [3]), i.e., from p [2] and q [3]. Similarly b After recovering all non-urgent sub-symbols in the previous steps, we can directly recover the urgent ones (i.e., the bottom two rows) using parity checks p II [t] for 8 < t ≤ 11.…”

Section: [−2] B[−1] and A[−1] Can Be Recovered From P[3] Q[4] And Pmentioning

confidence: 99%

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Diversity Embedded Streaming Erasure Codes (DE-SCo): Constructions and Optimality

Badr

Khisti

Martinian

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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Abstract-Streaming erasure codes encode a source stream to guarantee that each source symbol is recovered within a fixed delay at the receiver over a burst-erasure channel. This paper introduces diversity embedded streaming erasure codes (DE-SCo), that provide a flexible tradeoff between the channel quality and receiver delay. When the channel conditions are good, the source stream is recovered with a low delay, whereas when the channel conditions are poor the source stream is still recovered, albeit with a larger delay. Information theoretic analysis of the underlying burst-erasure broadcast channel reveals that DESCo achieve the minimum possible delay for the weaker user, without sacrificing the performance of the stronger user. Our constructions are explicit, incur polynomial time encoding and decoding complexity and outperform random linear codes over bursty erasure channels.

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Section: Background: Streaming Codes (Sco)mentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

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Diversity Embedded Streaming Erasure Codes (DE-SCo): Constructions and Optimality

Badr

Khisti

Martinian

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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“…Low-delay error correction codes for streaming sources have been recently studied in [3]- [5] and further generalized in [6], [7]. The focus in [3]- [5] was on burst-erasure channels.…”

Section: Introductionmentioning

confidence: 99%

“…The focus in [3]- [5] was on burst-erasure channels. The transmitter is required to encode a stream of source packets sequentially and the receiver is required to reconstruct each source packet in the stream with a fixed delay.…”

Section: Introductionmentioning

confidence: 99%

Delay-optimal streaming codes under source-channel rate mismatch

Patil

Badr

Khisti

et al. 2013

2013 Asilomar Conference on Signals, Systems and Computers

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We study low-delay error correction codes for streaming-recovery over a class of packet-erasure channels. In our setup, the encoder observes one source frame every M time slots, but is required to transmit a channel packet in each time slot. The decoder is required to reconstruct each source frame within a playback delay of T source frames. The collection of M transmitted channel packets between successive source frames is called a (channel) macro-packet. For a certain class of bursterasure channels, we characterize the associated capacity and develop explicit codes that attain the capacity. We recover as a special case, the capacity when M = 1, studied in earlier works. Our proposed code constructions involve splitting each source frame into two groups of sub-symbols, applying unequal error protection and carefully allocating source and parity-check sub-symbols within each macro-packet. Our constructions are a non-trivial extension of the previously proposed codes for M = 1.Simulation results indicate significant gains over baseline error correction codes for the Gilbert model for burst erasures.

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