480 research outputs found
Delay Minimization for Instantly Decodable Network Coding in Persistent Channels with Feedback Intermittence
In this paper, we consider the problem of minimizing the multicast decoding
delay of generalized instantly decodable network coding (G-IDNC) over
persistent forward and feedback erasure channels with feedback intermittence.
In such an environment, the sender does not always receive acknowledgement from
the receivers after each transmission. Moreover, both the forward and feedback
channels are subject to persistent erasures, which can be modelled by a two
state (good and bad states) Markov chain known as Gilbert-Elliott channel
(GEC). Due to such feedback imperfections, the sender is unable to determine
subsequent instantly decodable packets combination for all receivers. Given
this harsh channel and feedback model, we first derive expressions for the
probability distributions of decoding delay increments and then employ these
expressions in formulating the minimum decoding problem in such environment as
a maximum weight clique problem in the G-IDNC graph. We also show that the
problem formulations in simpler channel and feedback models are special cases
of our generalized formulation. Since this problem is NP-hard, we design a
greedy algorithm to solve it and compare it to blind approaches proposed in
literature. Through extensive simulations, our adaptive algorithm is shown to
outperform the blind approaches in all situations and to achieve significant
improvement in the decoding delay, especially when the channel is highly
persisten
Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines
We study adaptive network coding (NC) for scheduling real-time traffic over a
single-hop wireless network. To meet the hard deadlines of real-time traffic,
it is critical to strike a balance between maximizing the throughput and
minimizing the risk that the entire block of coded packets may not be decodable
by the deadline. Thus motivated, we explore adaptive NC, where the block size
is adapted based on the remaining time to the deadline, by casting this
sequential block size adaptation problem as a finite-horizon Markov decision
process. One interesting finding is that the optimal block size and its
corresponding action space monotonically decrease as the deadline approaches,
and the optimal block size is bounded by the "greedy" block size. These unique
structures make it possible to narrow down the search space of dynamic
programming, building on which we develop a monotonicity-based backward
induction algorithm (MBIA) that can solve for the optimal block size in
polynomial time. Since channel erasure probabilities would be time-varying in a
mobile network, we further develop a joint real-time scheduling and channel
learning scheme with adaptive NC that can adapt to channel dynamics. We also
generalize the analysis to multiple flows with hard deadlines and long-term
delivery ratio constraints, devise a low-complexity online scheduling algorithm
integrated with the MBIA, and then establish its asymptotical
throughput-optimality. In addition to analysis and simulation results, we
perform high fidelity wireless emulation tests with real radio transmissions to
demonstrate the feasibility of the MBIA in finding the optimal block size in
real time.Comment: 11 pages, 13 figure
On Minimizing the Maximum Broadcast Decoding Delay for Instantly Decodable Network Coding
In this paper, we consider the problem of minimizing the maximum broadcast
decoding delay experienced by all the receivers of generalized instantly
decodable network coding (IDNC). Unlike the sum decoding delay, the maximum
decoding delay as a definition of delay for IDNC allows a more equitable
distribution of the delays between the different receivers and thus a better
Quality of Service (QoS). In order to solve this problem, we first derive the
expressions for the probability distributions of maximum decoding delay
increments. Given these expressions, we formulate the problem as a maximum
weight clique problem in the IDNC graph. Although this problem is known to be
NP-hard, we design a greedy algorithm to perform effective packet selection.
Through extensive simulations, we compare the sum decoding delay and the max
decoding delay experienced when applying the policies to minimize the sum
decoding delay [1] and our policy to reduce the max decoding delay. Simulations
results show that our policy gives a good agreement among all the delay aspects
in all situations and outperforms the sum decoding delay policy to effectively
minimize the sum decoding delay when the channel conditions become harsher.
They also show that our definition of delay significantly improve the number of
served receivers when they are subject to strict delay constraints
Completion Time Reduction in Instantly Decodable Network Coding Through Decoding Delay Control
For several years, the completion time and decoding delay problems in
Instantly Decodable Network Coding (IDNC) were considered separately and were
thought to completely act against each other. Recently, some works aimed to
balance the effects of these two important IDNC metrics but none of them
studied a further optimization of one by controlling the other. In this paper,
we study the effect of controlling the decoding delay to reduce the completion
time below its currently best known solution. We first derive the
decoding-delay-dependent expressions of the users' and overall completion
times. Although using such expressions to find the optimal overall completion
time is NP-hard, we design a novel heuristic that minimizes the probability of
increasing the maximum of these decoding-delay-dependent completion time
expressions after each transmission through a layered control of their decoding
delays. Simulation results show that this new algorithm achieves both a lower
mean completion time and mean decoding delay compared to the best known
heuristic for completion time reduction. The gap in performance becomes
significant for harsh erasure scenarios
Joint Coding and Scheduling Optimization in Wireless Systems with Varying Delay Sensitivities
Throughput and per-packet delay can present strong trade-offs that are
important in the cases of delay sensitive applications.We investigate such
trade-offs using a random linear network coding scheme for one or more
receivers in single hop wireless packet erasure broadcast channels. We capture
the delay sensitivities across different types of network applications using a
class of delay metrics based on the norms of packet arrival times. With these
delay metrics, we establish a unified framework to characterize the rate and
delay requirements of applications and optimize system parameters. In the
single receiver case, we demonstrate the trade-off between average packet
delay, which we view as the inverse of throughput, and maximum ordered
inter-arrival delay for various system parameters. For a single broadcast
channel with multiple receivers having different delay constraints and feedback
delays, we jointly optimize the coding parameters and time-division scheduling
parameters at the transmitters. We formulate the optimization problem as a
Generalized Geometric Program (GGP). This approach allows the transmitters to
adjust adaptively the coding and scheduling parameters for efficient allocation
of network resources under varying delay constraints. In the case where the
receivers are served by multiple non-interfering wireless broadcast channels,
the same optimization problem is formulated as a Signomial Program, which is
NP-hard in general. We provide approximation methods using successive
formulation of geometric programs and show the convergence of approximations.Comment: 9 pages, 10 figure
On Throughput and Decoding Delay Performance of Instantly Decodable Network Coding
In this paper, a comprehensive study of packet-based instantly decodable
network coding (IDNC) for single-hop wireless broadcast is presented. The
optimal IDNC solution in terms of throughput is proposed and its packet
decoding delay performance is investigated. Lower and upper bounds on the
achievable throughput and decoding delay performance of IDNC are derived and
assessed through extensive simulations. Furthermore, the impact of receivers'
feedback frequency on the performance of IDNC is studied and optimal IDNC
solutions are proposed for scenarios where receivers' feedback is only
available after and IDNC round, composed of several coded transmissions.
However, since finding these IDNC optimal solutions is computational complex,
we further propose simple yet efficient heuristic IDNC algorithms. The impact
of system settings and parameters such as channel erasure probability, feedback
frequency, and the number of receivers is also investigated and simple
guidelines for practical implementations of IDNC are proposed.Comment: This is a 14-page paper submitted to IEEE/ACM Transaction on
Networking. arXiv admin note: text overlap with arXiv:1208.238
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