17,267 research outputs found
Statistical Analysis of Video Frame Size Distribution Originating from Scalable Video Codec (SVC)
Designing an effective and high performance network requires an accurate characterization and modeling of network traffic.The
modeling of video frame sizes is normally applied in simulation studies and mathematical analysis and generating streams for
testing and compliance purposes. Besides, video traffic assumed as a major source of multimedia traffic in future heterogeneous
network. Therefore, the statistical distribution of video data can be used as the inputs for performance modeling of networks. The
finding of this paper comprises the theoretical definition of distribution which seems to be relevant to the video trace in terms of
its statistical properties and finds the best distribution using both the graphical method and the hypothesis test.The data set used
in this article consists of layered video traces generating from Scalable Video Codec (SVC) video compression technique of three
different movies
Markovian Characterisation of H.264/SVC scalable video
In this paper, a multivariate Markovian traffic: model is proposed to characterise H.264/SVC scalable video traces. Parametrisation by a genetic algorithm results in models with a limited state space which accurately capture. both the temporal and the inter-layer correlation of the traces. A simulation study further shows that the model is capable of predicting performance of video streaming in various networking scenarios
A genetic approach to Markovian characterisation of H.264 scalable video
We propose an algorithm for multivariate Markovian characterisation of H.264/SVC scalable video traces at the sub-GoP (Group of Pictures) level. A genetic algorithm yields Markov models with limited state space that accurately capture temporal and inter-layer correlation. Key to our approach is the covariance-based fitness function. In comparison with the classical Expectation Maximisation algorithm, ours is capable of matching the second order statistics more accurately at the cost of less accuracy in matching the histograms of the trace. Moreover, a simulation study shows that our approach outperforms Expectation Maximisation in predicting performance of video streaming in various networking scenarios
The DRIVE-SAFE project: signal processing and advanced information technologies for improving driving prudence and accidents
In this paper, we will talk about the Drivesafe project whose aim is creating conditions for prudent driving on highways and roadways with the purposes of reducing accidents caused by driver behavior. To achieve these primary goals, critical data is being collected from multimodal sensors (such as cameras, microphones, and other sensors) to build a unique databank on driver behavior. We are developing system and technologies for analyzing the data and automatically determining potentially dangerous situations (such as driver fatigue, distraction, etc.). Based on the findings from these studies, we will propose systems for warning the drivers and taking other precautionary measures to avoid accidents once a dangerous situation is detected. In order to address these issues a national consortium has been formed including Automotive Research Center (OTAM), Koç University, Istanbul Technical University, Sabancı University, Ford A.S., Renault A.S., and Fiat A. Ş
The Price of Fog: a Data-Driven Study on Caching Architectures in Vehicular Networks
Vehicular users are expected to consume large amounts of data, for both
entertainment and navigation purposes. This will put a strain on cellular
networks, which will be able to cope with such a load only if proper caching is
in place, this in turn begs the question of which caching architecture is the
best-suited to deal with vehicular content consumption. In this paper, we
leverage a large-scale, crowd-collected trace to (i) characterize the vehicular
traffic demand, in terms of overall magnitude and content breakup, (ii) assess
how different caching approaches perform against such a real-world load, (iii)
study the effect of recommendation systems and local contents. We define a
price-of-fog metric, expressing the additional caching capacity to deploy when
moving from traditional, centralized caching architectures to a "fog computing"
approach, where caches are closer to the network edge. We find that for
location-specific contents, such as the ones that vehicular users are most
likely to request, such a price almost disappears. Vehicular networks thus make
a strong case for the adoption of mobile-edge caching, as we are able to reap
the benefit thereof -- including a reduction in the distance traveled by data,
within the core network -- with little or no of the associated disadvantages.Comment: ACM IoV-VoI 2016 MobiHoc Workshop, The 17th ACM International
Symposium on Mobile Ad Hoc Networking and Computing: MobiHoc 2016-IoV-VoI
Workshop, Paderborn, German
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