6,253 research outputs found
Infocast: A New Paradigm for Collaborative Content Distribution from Roadside Units to Vehicular Networks Using Rateless Codes
In this paper, we address the problem of distributing a large amount of bulk
data to a sparse vehicular network from roadside infostations, using efficient
vehicle-to-vehicle collaboration. Due to the highly dynamic nature of the
underlying vehicular network topology, we depart from architectures requiring
centralized coordination, reliable MAC scheduling, or global network state
knowledge, and instead adopt a distributed paradigm with simple protocols. In
other words, we investigate the problem of reliable dissemination from multiple
sources when each node in the network shares a limited amount of its resources
for cooperating with others. By using \emph{rateless} coding at the Road Side
Unit (RSU) and using vehicles as data carriers, we describe an efficient way to
achieve reliable dissemination to all nodes (even disconnected clusters in the
network). In the nutshell, we explore vehicles as mobile storage devices. We
then develop a method to keep the density of the rateless codes packets as a
function of distance from the RSU at the desired level set for the target
decoding distance. We investigate various tradeoffs involving buffer size,
maximum capacity, and the mobility parameter of the vehicles
Random Linear Network Coding for 5G Mobile Video Delivery
An exponential increase in mobile video delivery will continue with the
demand for higher resolution, multi-view and large-scale multicast video
services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a
number of new opportunities for optimizing video delivery across both 5G core
and radio access networks. One of the promising approaches for video quality
adaptation, throughput enhancement and erasure protection is the use of
packet-level random linear network coding (RLNC). In this review paper, we
discuss the integration of RLNC into the 5G NR standard, building upon the
ideas and opportunities identified in 4G LTE. We explicitly identify and
discuss in detail novel 5G NR features that provide support for RLNC-based
video delivery in 5G, thus pointing out to the promising avenues for future
research.Comment: Invited paper for Special Issue "Network and Rateless Coding for
Video Streaming" - MDPI Informatio
Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures
Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs
Model-driven Scheduling for Distributed Stream Processing Systems
Distributed Stream Processing frameworks are being commonly used with the
evolution of Internet of Things(IoT). These frameworks are designed to adapt to
the dynamic input message rate by scaling in/out.Apache Storm, originally
developed by Twitter is a widely used stream processing engine while others
includes Flink, Spark streaming. For running the streaming applications
successfully there is need to know the optimal resource requirement, as
over-estimation of resources adds extra cost.So we need some strategy to come
up with the optimal resource requirement for a given streaming application. In
this article, we propose a model-driven approach for scheduling streaming
applications that effectively utilizes a priori knowledge of the applications
to provide predictable scheduling behavior. Specifically, we use application
performance models to offer reliable estimates of the resource allocation
required. Further, this intuition also drives resource mapping, and helps
narrow the estimated and actual dataflow performance and resource utilization.
Together, this model-driven scheduling approach gives a predictable application
performance and resource utilization behavior for executing a given DSPS
application at a target input stream rate on distributed resources.Comment: 54 page
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