Erasure coding for real-time streaming

Leong, Derek; Ho, Tracey

doi:10.1109/isit.2012.6284055

Cited by 62 publications

(29 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fig.3(b). This is the very kind of stream discussed in [4]. According to [4,Theorem 3], the upper bound on message sizes is s h ≤ d−z h j=1 y j , where y j is as defined in [4].…”

Section: Example 3 Stream a Is A Real-time Stream As Shown Inmentioning

confidence: 99%

See 1 more Smart Citation

Priority-Based Real-Time Stream Coding under Burst Erasures

Wei¹,

Mao²,

Leng³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

Self Cite

View full text Add to dashboard Cite

The real-time streams play an increasingly important role in both traditional and emerging networks. Whereas, transmitting such streams requires vast channel resources, posing a huge challenge to the networks. Motivated by this problem, this paper considers a real-time stream system, where realtime messages with different importance should be transmitted through a burst erasure channel, and be decoded by the receiver within a fixed delay. We introduce the concepts of Symmetric Real-time (SR) stream and Asymmetric Real-time (AR) stream according to the stream characteristics. Different from the classical Shannon capacity, the channel transmission capacities are derived for SR and AR streams respectively, which can be used to guide the design of efficient real-time stream coding approaches. The capacity for SR streams is shown asymptotically achievable with a Proportional Time-invariant Intra-session Code (PTIC). Moreover, a Maximum Time-invariant Intra-session Code (MTIC) is presented for AR streams, which can also asymptotically achieve the capacity. Finally, the performances of PTIC and MTIC are verified by simulation experiments.

show abstract

“…Fig.3(b). This is the very kind of stream discussed in [4]. According to [4,Theorem 3], the upper bound on message sizes is s h ≤ d−z h j=1 y j , where y j is as defined in [4].…”

Section: Example 3 Stream a Is A Real-time Stream As Shown Inmentioning

confidence: 99%

“…So far only a few works tried to find the accurate RST capacity and design corresponding codes. Leong and Ho [3] [4] presented a symmetric intra-session code for real-time streams. Another coding scheme presented by Martinian and Badr in [5] [6] can adjust the delay deadline according to the channel quality.…”

Section: Introductionmentioning

confidence: 99%

Priority-Based Real-Time Stream Coding under Burst Erasures

Wei¹,

Mao²,

Leng³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition to distributed storage, the symmetric allocation algorithms can also be applied to the problem of code design for real-time streaming [7], [8], where messages arrive sequentially at the source and are encoded for transmission over a packet erasure channel to a sink, which needs to decode the messages sequentially within a specified delay. In various network scenarios, packet delay exhibits variation, causing the probability of packet reception to increase with delay.…”

Section: Algorithm Application: Symmetric Time-invariant Intrasessmentioning

confidence: 99%

Distributed storage allocation for heterogeneous systems

Zhao

Leong

et al. 2013

2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton)

Self Cite

View full text Add to dashboard Cite

We consider a distributed storage system where the storage nodes have heterogeneous access probabilities. The problem is to allocate a given storage budget across the nodes so as to store a unit-size data object with maximum reliability. We propose efficient algorithms for optimizing over several classes of allocations. In the basic one-level symmetric allocation, the storage budget is spread evenly over an appropriately chosen subset of nodes. In the multi k-level symmetric allocation, the budget is divided into k parts, each spread evenly over a different subset of nodes, such that the amount allocated to each node in the higher levels is multiple times that of the last level. These allocations are simpler and are shown to outperform existing methods in numerical experiments. We also describe an application of the symmetric allocations to the design of streaming codes.

show abstract

“…Robust extensions that are resilient against both burst and isolated losses are reported in [8]. For other related works on lowdelay streaming codes we refer to [9]- [16].…”

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

View full text Add to dashboard Cite

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.

show abstract

Erasure coding for real-time streaming

Cited by 62 publications

References 4 publications

Priority-Based Real-Time Stream Coding under Burst Erasures

Priority-Based Real-Time Stream Coding under Burst Erasures

Distributed storage allocation for heterogeneous systems

Delay-optimal streaming codes under source-channel rate mismatch

Contact Info

Product

Resources

About