789 research outputs found
Energy-efficient bandwidth allocation for multiuser scalable video streaming over WLAN
We consider the problem of packet scheduling for the transmission of multiple video streams over a wireless local area network (WLAN). A cross-layer optimization framework is proposed to minimize the wireless transceiver energy consumption while meeting the user required visual quality constraints. The framework relies on the IEEE 802.11 standard and on the embedded bitstream structure of the scalable video coding scheme. It integrates an application-level video quality metric as QoS constraint (instead of a communication layer quality metric) with energy consumption optimization through link layer scaling and sleeping. Both energy minimization and min-max energy optimization strategies are discussed. Simulation results demonstrate significant energy gains compared to the state-of-the-art approaches
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Multicast networks : capacity, algorithms, and implementation
textIn this dissertation, we investigate the capacity and performance of wireless networks with an emphasis on multicast traffic. The defining characteristic of a multicast network is a network where a number of different destinations all require the information generated by a single source. The models that we explore differ in the nature of the nodes from all-mobile case where all nodes are mobile to hybrid case where some nodes are mobile and some are static. We investigate different performance measure for these wireless multicast networks: upper bounds, capacity scaling laws, and achievable rates. The understanding of these measures for such networks helps in the development of efficient algorithms for operating these networks.
In addition, we study the practical realization of algorithms for real-time streaming of rich multimedia content in the context of mobile wireless networks for embedded and cyberphysical systems. Our initial work is in the context of unicast and multiple unicast systems over an autonomous aerial vehicle (AAV) network. Bandwidth requirements and stringent delay constraints of real-time video streaming, paired with limitations on computational complexity and power consumptions imposed by the underlying implementation platform, make cross-layer and cross-domain co-design approaches a necessity. In this dissertation, we propose a novel, low-complexity rate-distortion optimized (RDO) protocol specifically targeted at video streaming over mobile embedded networks. First, we test the performance of our RDO algorithm on simulation models developed for aerial mobility of multiple wirelessly communicating AAVs. Second, we test the performance of our RDO algorithm and other proposed adaptive algorithms on a real network of AAVs and present a comparative study between these different algorithms. Note that generalizing these algorithms to multicast settings is relatively straightforward and thus is not highlighted to a great degree in this thesis.Electrical and Computer Engineerin
End to end Multi-Objective Optimisation of H.264 and HEVC Codecs
All multimedia devices now incorporate video CODECs that comply with international video coding standards such as H.264 / MPEG4-AVC and the new High Efficiency Video Coding Standard (HEVC) otherwise known as H.265. Although the standard CODECs have been designed to include algorithms with optimal efficiency, large number of coding parameters can be used to fine tune their operation, within known constraints of for e.g., available computational power, bandwidth, consumer QoS requirements, etc. With large number of such parameters involved, determining which parameters will play a significant role in providing optimal quality of service within given constraints is a further challenge that needs to be met. Further how to select the values of the significant parameters so that the CODEC performs optimally under the given constraints is a further important question to be answered.
This thesis proposes a framework that uses machine learning algorithms to model the performance of a video CODEC based on the significant coding parameters. Means of modelling both the Encoder and Decoder performance is proposed. We define objective functions that can be used to model the performance related properties of a CODEC, i.e., video quality, bit-rate and CPU time. We show that these objective functions can be practically utilised in video Encoder/Decoder designs, in particular in their performance optimisation within given operational and practical constraints. A Multi-objective Optimisation framework based on Genetic Algorithms is thus proposed to optimise the performance of a video codec. The framework is designed to jointly minimize the CPU Time, Bit-rate and to maximize the quality of the compressed video stream. The thesis presents the use of this framework in the performance modelling and multi-objective optimisation of the most widely used video coding standard in practice at present, H.264 and the latest video coding standard, H.265/HEVC.
When a communication network is used to transmit video, performance related parameters of the communication channel will impact the end-to-end performance of the video CODEC. Network delays and packet loss will impact the quality of the video that is received at the decoder via the communication channel, i.e., even if a video CODEC is optimally configured network conditions will make the experience sub-optimal. Given the above the thesis proposes a design, integration and testing of a novel approach to simulating a wired network and the use of UDP protocol for the transmission of video data. This network is subsequently used to simulate the impact of packet loss and network delays on optimally coded video based on the framework previously proposed for the modelling and optimisation of video CODECs. The quality of received video under different levels of packet loss and network delay is simulated, concluding the impact on transmitted video based on their content and features
Efficient Bitrate Ladder Construction for Content-Optimized Adaptive Video Streaming
One of the challenges faced by many video providers is the heterogeneity of
network specifications, user requirements, and content compression performance.
The universal solution of a fixed bitrate ladder is inadequate in ensuring a
high quality of user experience without re-buffering or introducing annoying
compression artifacts. However, a content-tailored solution, based on
extensively encoding across all resolutions and over a wide quality range is
highly expensive in terms of computational, financial, and energy costs.
Inspired by this, we propose an approach that exploits machine learning to
predict a content-optimized bitrate ladder. The method extracts spatio-temporal
features from the uncompressed content, trains machine-learning models to
predict the Pareto front parameters, and, based on that, builds the ladder
within a defined bitrate range. The method has the benefit of significantly
reducing the number of encodes required per sequence. The presented results,
based on 100 HEVC-encoded sequences, demonstrate a reduction in the number of
encodes required when compared to an exhaustive search and an
interpolation-based method, by 89.06% and 61.46%, respectively, at the cost of
an average Bj{\o}ntegaard Delta Rate difference of 1.78% compared to the
exhaustive approach. Finally, a hybrid method is introduced that selects either
the proposed or the interpolation-based method depending on the sequence
features. This results in an overall 83.83% reduction of required encodings at
the cost of an average Bj{\o}ntegaard Delta Rate difference of 1.26%
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
Content-Aware Multimedia Communications
The demands for fast, economic and reliable dissemination of multimedia
information are steadily growing within our society. While people and
economy increasingly rely on communication technologies, engineers still
struggle with their growing complexity.
Complexity in multimedia communication originates from several sources. The
most prominent is the unreliability of packet networks like the Internet.
Recent advances in scheduling and error control mechanisms for streaming
protocols have shown that the quality and robustness of multimedia delivery
can be improved significantly when protocols are aware of the content they
deliver. However, the proposed mechanisms require close cooperation between
transport systems and application layers which increases the overall system
complexity. Current approaches also require expensive metrics and focus on
special encoding formats only. A general and efficient model is missing so
far.
This thesis presents efficient and format-independent solutions to support
cross-layer coordination in system architectures. In particular, the first
contribution of this work is a generic dependency model that enables
transport layers to access content-specific properties of media streams,
such as dependencies between data units and their importance. The second
contribution is the design of a programming model for streaming
communication and its implementation as a middleware architecture. The
programming model hides the complexity of protocol stacks behind simple
programming abstractions, but exposes cross-layer control and monitoring
options to application programmers. For example, our interfaces allow
programmers to choose appropriate failure semantics at design time while
they can refine error protection and visibility of low-level errors at
run-time.
Based on some examples we show how our middleware simplifies the
integration of stream-based communication into large-scale application
architectures. An important result of this work is that despite cross-layer
cooperation, neither application nor transport protocol designers
experience an increase in complexity. Application programmers can even
reuse existing streaming protocols which effectively increases system
robustness.Der Bedarf unsere Gesellschaft nach kostengünstiger und
zuverlässiger
Kommunikation wächst stetig. Während wir uns selbst immer mehr von modernen
Kommunikationstechnologien abhängig machen, müssen die Ingenieure dieser
Technologien sowohl den Bedarf nach schneller Einführung neuer Produkte
befriedigen als auch die wachsende Komplexität der Systeme beherrschen.
Gerade die Übertragung multimedialer Inhalte wie Video und Audiodaten ist
nicht trivial. Einer der prominentesten Gründe dafür ist die
Unzuverlässigkeit heutiger Netzwerke, wie z.B.~dem Internet. Paketverluste
und schwankende Laufzeiten können die Darstellungsqualität massiv
beeinträchtigen. Wie jüngste Entwicklungen im Bereich der
Streaming-Protokolle zeigen, sind jedoch Qualität und Robustheit der
Übertragung effizient kontrollierbar, wenn Streamingprotokolle
Informationen über den Inhalt der transportierten Daten ausnutzen.
Existierende Ansätze, die den Inhalt von Multimediadatenströmen
beschreiben, sind allerdings meist auf einzelne Kompressionsverfahren
spezialisiert und verwenden berechnungsintensive Metriken. Das reduziert
ihren praktischen Nutzen deutlich. Außerdem erfordert der
Informationsaustausch eine enge Kooperation zwischen Applikationen und
Transportschichten. Da allerdings die Schnittstellen aktueller
Systemarchitekturen nicht darauf vorbereitet sind, müssen entweder die
Schnittstellen erweitert oder alternative Architekturkonzepte geschaffen
werden. Die Gefahr beider Varianten ist jedoch, dass sich die Komplexität
eines Systems dadurch weiter erhöhen kann.
Das zentrale Ziel dieser Dissertation ist es deshalb,
schichtenübergreifende Koordination bei gleichzeitiger Reduzierung der
Komplexität zu erreichen. Hier leistet die Arbeit zwei Beträge zum
aktuellen Stand der Forschung. Erstens definiert sie ein universelles
Modell zur Beschreibung von Inhaltsattributen, wie Wichtigkeiten und
Abhängigkeitsbeziehungen innerhalb eines Datenstroms. Transportschichten
können dieses Wissen zur effizienten Fehlerkontrolle verwenden. Zweitens
beschreibt die Arbeit das Noja Programmiermodell für multimediale
Middleware. Noja definiert Abstraktionen zur Übertragung und Kontrolle
multimedialer Ströme, die die Koordination von Streamingprotokollen mit
Applikationen ermöglichen. Zum Beispiel können Programmierer geeignete
Fehlersemantiken und Kommunikationstopologien auswählen und den konkreten
Fehlerschutz dann zur Laufzeit verfeinern und kontrolliere
Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression
In order to ensure optimal quality of experience toward end users during video streaming, automatic video quality assessment becomes an important field-of-interest to video service providers. Objective video quality metrics try to estimate perceived quality with high accuracy and in an automated manner. In traditional approaches, these metrics model the complex properties of the human visual system. More recently, however, it has been shown that machine learning approaches can also yield competitive results. In this paper, we present a novel no-reference bitstream-based objective video quality metric that is constructed by genetic programming-based symbolic regression. A key benefit of this approach is that it calculates reliable white-box models that allow us to determine the importance of the parameters. Additionally, these models can provide human insight into the underlying principles of subjective video quality assessment. Numerical results show that perceived quality can be modeled with high accuracy using only parameters extracted from the received video bitstream
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