2,610 research outputs found
Energy Consumption Of Visual Sensor Networks: Impact Of Spatio-Temporal Coverage
Wireless visual sensor networks (VSNs) are expected to play a major role in
future IEEE 802.15.4 personal area networks (PAN) under recently-established
collision-free medium access control (MAC) protocols, such as the IEEE
802.15.4e-2012 MAC. In such environments, the VSN energy consumption is
affected by the number of camera sensors deployed (spatial coverage), as well
as the number of captured video frames out of which each node processes and
transmits data (temporal coverage). In this paper, we explore this aspect for
uniformly-formed VSNs, i.e., networks comprising identical wireless visual
sensor nodes connected to a collection node via a balanced cluster-tree
topology, with each node producing independent identically-distributed
bitstream sizes after processing the video frames captured within each network
activation interval. We derive analytic results for the energy-optimal
spatio-temporal coverage parameters of such VSNs under a-priori known bounds
for the number of frames to process per sensor and the number of nodes to
deploy within each tier of the VSN. Our results are parametric to the
probability density function characterizing the bitstream size produced by each
node and the energy consumption rates of the system of interest. Experimental
results reveal that our analytic results are always within 7% of the energy
consumption measurements for a wide range of settings. In addition, results
obtained via a multimedia subsystem show that the optimal spatio-temporal
settings derived by the proposed framework allow for substantial reduction of
energy consumption in comparison to ad-hoc settings. As such, our analytic
modeling is useful for early-stage studies of possible VSN deployments under
collision-free MAC protocols prior to costly and time-consuming experiments in
the field.Comment: to appear in IEEE Transactions on Circuits and Systems for Video
Technology, 201
Enabling Quality-Driven Scalable Video Transmission over Multi-User NOMA System
Recently, non-orthogonal multiple access (NOMA) has been proposed to achieve
higher spectral efficiency over conventional orthogonal multiple access.
Although it has the potential to meet increasing demands of video services, it
is still challenging to provide high performance video streaming. In this
research, we investigate, for the first time, a multi-user NOMA system design
for video transmission. Various NOMA systems have been proposed for data
transmission in terms of throughput or reliability. However, the perceived
quality, or the quality-of-experience of users, is more critical for video
transmission. Based on this observation, we design a quality-driven scalable
video transmission framework with cross-layer support for multi-user NOMA. To
enable low complexity multi-user NOMA operations, a novel user grouping
strategy is proposed. The key features in the proposed framework include the
integration of the quality model for encoded video with the physical layer
model for NOMA transmission, and the formulation of multi-user NOMA-based video
transmission as a quality-driven power allocation problem. As the problem is
non-concave, a global optimal algorithm based on the hidden monotonic property
and a suboptimal algorithm with polynomial time complexity are developed.
Simulation results show that the proposed multi-user NOMA system outperforms
existing schemes in various video delivery scenarios.Comment: 9 pages, 6 figures. This paper has already been accepted by IEEE
INFOCOM 201
RBF-Based QP Estimation Model for VBR Control in H.264/SVC
In this paper we propose a novel variable bit rate (VBR) controller for real-time H.264/scalable video coding (SVC) applications. The proposed VBR controller relies on the fact that consecutive pictures within the same scene often exhibit similar degrees of complexity, and consequently should be encoded using similar quantization parameter (QP) values for the sake of quality consistency. In oder to prevent unnecessary QP fluctuations, the proposed VBR controller allows for just an incremental variation of QP with respect to that of the previous picture, focusing on the design of an effective method for estimating this QP variation. The implementation in H.264/SVC requires to locate a rate controller at each dependency layer (spatial or coarse grain scalability). In particular, the QP increment estimation at each layer is computed by means of a radial basis function (RBF) network that is specially designed for this purpose. Furthermore, the RBF network design process was conceived to provide an effective solution for a wide range of practical real-time VBR applications for scalable video content delivery. In order to assess the proposed VBR controller, two real-time application scenarios were simulated: mobile live streaming and IPTV broadcast. It was compared to constant QP encoding and a recently proposed constant bit rate (CBR) controller for H.264/SVC. The experimental results show that the proposed method achieves remarkably consistent quality, outperforming the reference CBR controller in the two scenarios for all the spatio-temporal resolutions considered.Proyecto CCG10-UC3M/TIC-5570 de la Comunidad Autónoma de Madrid y Universidad Carlos III de MadridPublicad
Task-Oriented Communication for Edge Video Analytics
With the development of artificial intelligence (AI) techniques and the
increasing popularity of camera-equipped devices, many edge video analytics
applications are emerging, calling for the deployment of computation-intensive
AI models at the network edge. Edge inference is a promising solution to move
the computation-intensive workloads from low-end devices to a powerful edge
server for video analytics, but the device-server communications will remain a
bottleneck due to the limited bandwidth. This paper proposes a task-oriented
communication framework for edge video analytics, where multiple devices
collect the visual sensory data and transmit the informative features to an
edge server for processing. To enable low-latency inference, this framework
removes video redundancy in spatial and temporal domains and transmits minimal
information that is essential for the downstream task, rather than
reconstructing the videos at the edge server. Specifically, it extracts compact
task-relevant features based on the deterministic information bottleneck (IB)
principle, which characterizes a tradeoff between the informativeness of the
features and the communication cost. As the features of consecutive frames are
temporally correlated, we propose a temporal entropy model (TEM) to reduce the
bitrate by taking the previous features as side information in feature
encoding. To further improve the inference performance, we build a
spatial-temporal fusion module at the server to integrate features of the
current and previous frames for joint inference. Extensive experiments on video
analytics tasks evidence that the proposed framework effectively encodes
task-relevant information of video data and achieves a better rate-performance
tradeoff than existing methods
In-layer multi-buffer framework for rate-controlled scalable video coding
Temporal scalability is supported in scalable video coding (SVC) by means of hierarchical prediction structures, where the higher layers can be ignored for frame rate reduction. Nevertheless, this kind of scalability is not totally exploited by the rate control (RC) algorithms since the hypothetical reference decoder (HRD) requirement is only satisfied for the highest frame rate sub-stream of every dependency (spatial or coarse grain scalability) layer. In this paper we propose a novel RC approach that aims to deliver several HRD-compliant temporal resolutions within a particular dependency layer. Instead of using the common SVC encoder configuration consisting of a dependency layer per each temporal resolution, a compact configuration that does not require additional dependency layers for providing different HRD-compliant temporal resolutions is proposed. Specifically, the proposed framework for rate-controlled SVC uses a set of virtual buffers within a dependency layer so that their levels can be simultaneously controlled for overflow and underflow prevention while minimizing the reconstructed video distortion of the corresponding sub-streams. This in-layer multi-buffer approach has been built on top of a baseline H.264/SVC RC algorithm for variable bit rate applications. The experimental results show that our proposal achieves a good performance in terms of mean quality, quality consistency, and buffer control using a reduced number of layers.This work has been partially supported by the National Grant TEC2011-26807 of the Spanish Ministry of Science and Innovation.Publicad
Adaptive sensing and optimal power allocation for wireless video sensors with sigma-delta imager
We consider optimal power allocation for wireless video sensors (WVSs), including the image sensor subsystem in the system analysis. By assigning a power-rate-distortion (P-R-D) characteristic for the image sensor, we build a comprehensive P-R-D optimization framework for WVSs. For a WVS node operating under a power budget, we propose power allocation among the image sensor, compression, and transmission modules, in order to minimize the distortion of the video reconstructed at the receiver. To demonstrate the proposed optimization method, we establish a P-R-D model for an image sensor based upon a pixel level sigma-delta ( ) image sensor design that allows investigation of the tradeoff between the bit depth of the captured images and spatio-temporal characteristics of the video sequence under the power constraint. The optimization results obtained in this setting confirm that including the image sensor in the system optimization procedure can improve the overall video quality under power constraint and prolong the lifetime of the WVSs. In particular, when the available power budget for a WVS node falls below a threshold, adaptive sensing becomes necessary to ensure that the node communicates useful information about the video content while meeting its power budget.Peer ReviewedPostprint (published version
Multi-View Video Packet Scheduling
In multiview applications, multiple cameras acquire the same scene from
different viewpoints and generally produce correlated video streams. This
results in large amounts of highly redundant data. In order to save resources,
it is critical to handle properly this correlation during encoding and
transmission of the multiview data. In this work, we propose a
correlation-aware packet scheduling algorithm for multi-camera networks, where
information from all cameras are transmitted over a bottleneck channel to
clients that reconstruct the multiview images. The scheduling algorithm relies
on a new rate-distortion model that captures the importance of each view in the
scene reconstruction. We propose a problem formulation for the optimization of
the packet scheduling policies, which adapt to variations in the scene content.
Then, we design a low complexity scheduling algorithm based on a trellis search
that selects the subset of candidate packets to be transmitted towards
effective multiview reconstruction at clients. Extensive simulation results
confirm the gain of our scheduling algorithm when inter-source correlation
information is used in the scheduler, compared to scheduling policies with no
information about the correlation or non-adaptive scheduling policies. We
finally show that increasing the optimization horizon in the packet scheduling
algorithm improves the transmission performance, especially in scenarios where
the level of correlation rapidly varies with time
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