536 research outputs found
Expander Chunked Codes
Chunked codes are efficient random linear network coding (RLNC) schemes with
low computational cost, where the input packets are encoded into small chunks
(i.e., subsets of the coded packets). During the network transmission, RLNC is
performed within each chunk. In this paper, we first introduce a simple
transfer matrix model to characterize the transmission of chunks, and derive
some basic properties of the model to facilitate the performance analysis. We
then focus on the design of overlapped chunked codes, a class of chunked codes
whose chunks are non-disjoint subsets of input packets, which are of special
interest since they can be encoded with negligible computational cost and in a
causal fashion. We propose expander chunked (EC) codes, the first class of
overlapped chunked codes that have an analyzable performance,where the
construction of the chunks makes use of regular graphs. Numerical and
simulation results show that in some practical settings, EC codes can achieve
rates within 91 to 97 percent of the optimum and outperform the
state-of-the-art overlapped chunked codes significantly.Comment: 26 pages, 3 figures, submitted for journal publicatio
In-Order Delivery Delay of Transport Layer Coding
A large number of streaming applications use reliable transport protocols
such as TCP to deliver content over the Internet. However, head-of-line
blocking due to packet loss recovery can often result in unwanted behavior and
poor application layer performance. Transport layer coding can help mitigate
this issue by helping to recover from lost packets without waiting for
retransmissions. We consider the use of an on-line network code that inserts
coded packets at strategic locations within the underlying packet stream. If
retransmissions are necessary, additional coding packets are transmitted to
ensure the receiver's ability to decode. An analysis of this scheme is provided
that helps determine both the expected in-order packet delivery delay and its
variance. Numerical results are then used to determine when and how many coded
packets should be inserted into the packet stream, in addition to determining
the trade-offs between reducing the in-order delay and the achievable rate. The
analytical results are finally compared with experimental results to provide
insight into how to minimize the delay of existing transport layer protocols
Sparse Network Coding with Overlapping Classes
This paper presents a novel approach to network coding for distribution of
large files. Instead of the usual approach of splitting packets into disjoint
classes (also known as generations) we propose the use of overlapping classes.
The overlapping allows the decoder to alternate between Gaussian elimination
and back substitution, simultaneously boosting the performance and reducing the
decoding complexity. Our approach can be seen as a combination of fountain
coding and network coding. Simulation results are presented that demonstrate
the promise of our approach.Comment: 15 pages, 5 figures, to be published at NetCod 200
Effects of the Generation Size and Overlap on Throughput and Complexity in Randomized Linear Network Coding
To reduce computational complexity and delay in randomized network coded
content distribution, and for some other practical reasons, coding is not
performed simultaneously over all content blocks, but over much smaller,
possibly overlapping subsets of these blocks, known as generations. A penalty
of this strategy is throughput reduction. To analyze the throughput loss, we
model coding over generations with random generation scheduling as a coupon
collector's brotherhood problem. This model enables us to derive the expected
number of coded packets needed for successful decoding of the entire content as
well as the probability of decoding failure (the latter only when generations
do not overlap) and further, to quantify the tradeoff between computational
complexity and throughput. Interestingly, with a moderate increase in the
generation size, throughput quickly approaches link capacity. Overlaps between
generations can further improve throughput substantially for relatively small
generation sizes.Comment: To appear in IEEE Transactions on Information Theory Special Issue:
Facets of Coding Theory: From Algorithms to Networks, Feb 201
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