6,839 research outputs found
Bounded Delay Scheduling with Packet Dependencies
A common situation occurring when dealing with multimedia traffic is having
large data frames fragmented into smaller IP packets, and having these packets
sent independently through the network. For real-time multimedia traffic,
dropping even few packets of a frame may render the entire frame useless. Such
traffic is usually modeled as having {\em inter-packet dependencies}. We study
the problem of scheduling traffic with such dependencies, where each packet has
a deadline by which it should arrive at its destination. Such deadlines are
common for real-time multimedia applications, and are derived from stringent
delay constraints posed by the application. The figure of merit in such
environments is maximizing the system's {\em goodput}, namely, the number of
frames successfully delivered.
We study online algorithms for the problem of maximizing goodput of
delay-bounded traffic with inter-packet dependencies, and use competitive
analysis to evaluate their performance. We present competitive algorithms for
the problem, as well as matching lower bounds that are tight up to a constant
factor. We further present the results of a simulation study which further
validates our algorithmic approach and shows that insights arising from our
analysis are indeed manifested in practice
Scheduling for Optimal Rate Allocation in Ad Hoc Networks With Heterogeneous Delay Constraints
This paper studies the problem of scheduling in single-hop wireless networks
with real-time traffic, where every packet arrival has an associated deadline
and a minimum fraction of packets must be transmitted before the end of the
deadline. Using optimization and stochastic network theory we propose a
framework to model the quality of service (QoS) requirements under delay
constraints. The model allows for fairly general arrival models with
heterogeneous constraints. The framework results in an optimal scheduling
algorithm which fairly allocates data rates to all flows while meeting
long-term delay demands. We also prove that under a simplified scenario our
solution translates into a greedy strategy that makes optimal decisions with
low complexity
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
Downlink Video Streaming for Users Non-Equidistant from Base Station
We consider multiuser video transmission for users that are non-equidistantly positioned from base station. We propose a greedy algorithm for video streaming in a wireless system with capacity achieving channel coding, that implements the cross-layer principle by partially separating the physical and the application layer. In such a system the parameters at the physical layer are dependent on the packet length and the conditions in the wireless channel and the parameters at the application layer are dependent on the reduction of the expected distortion assuming no packet errors in the system. We also address the fairness in the multiuser video system with non-equidistantly positioned users. Our fairness algorithm is based on modified opportunistic round robin scheduling. We evaluate the performance of the proposed algorithms by simulating the transmission of H.264/AVC video signals in a TDMA wireless system
Achieving Optimal Throughput and Near-Optimal Asymptotic Delay Performance in Multi-Channel Wireless Networks with Low Complexity: A Practical Greedy Scheduling Policy
In this paper, we focus on the scheduling problem in multi-channel wireless
networks, e.g., the downlink of a single cell in fourth generation (4G)
OFDM-based cellular networks. Our goal is to design practical scheduling
policies that can achieve provably good performance in terms of both throughput
and delay, at a low complexity. While a class of -complexity
hybrid scheduling policies are recently developed to guarantee both
rate-function delay optimality (in the many-channel many-user asymptotic
regime) and throughput optimality (in the general non-asymptotic setting),
their practical complexity is typically high. To address this issue, we develop
a simple greedy policy called Delay-based Server-Side-Greedy (D-SSG) with a
\lower complexity , and rigorously prove that D-SSG not only achieves
throughput optimality, but also guarantees near-optimal asymptotic delay
performance. Specifically, we show that the rate-function attained by D-SSG for
any delay-violation threshold , is no smaller than the maximum achievable
rate-function by any scheduling policy for threshold . Thus, we are able
to achieve a reduction in complexity (from of the hybrid
policies to ) with a minimal drop in the delay performance. More
importantly, in practice, D-SSG generally has a substantially lower complexity
than the hybrid policies that typically have a large constant factor hidden in
the notation. Finally, we conduct numerical simulations to validate
our theoretical results in various scenarios. The simulation results show that
D-SSG not only guarantees a near-optimal rate-function, but also empirically is
virtually indistinguishable from delay-optimal policies.Comment: Accepted for publication by the IEEE/ACM Transactions on Networking,
February 2014. A preliminary version of this work was presented at IEEE
INFOCOM 2013, Turin, Italy, April 201
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