2,409 research outputs found
vSkyConf: Cloud-assisted Multi-party Mobile Video Conferencing
As an important application in the busy world today, mobile video
conferencing facilitates virtual face-to-face communication with friends,
families and colleagues, via their mobile devices on the move. However, how to
provision high-quality, multi-party video conferencing experiences over mobile
devices is still an open challenge. The fundamental reason behind is the lack
of computation and communication capacities on the mobile devices, to scale to
large conferencing sessions. In this paper, we present vSkyConf, a
cloud-assisted mobile video conferencing system to fundamentally improve the
quality and scale of multi-party mobile video conferencing. By novelly
employing a surrogate virtual machine in the cloud for each mobile user, we
allow fully scalable communication among the conference participants via their
surrogates, rather than directly. The surrogates exchange conferencing streams
among each other, transcode the streams to the most appropriate bit rates, and
buffer the streams for the most efficient delivery to the mobile recipients. A
fully decentralized, optimal algorithm is designed to decide the best paths of
streams and the most suitable surrogates for video transcoding along the paths,
such that the limited bandwidth is fully utilized to deliver streams of the
highest possible quality to the mobile recipients. We also carefully tailor a
buffering mechanism on each surrogate to cooperate with optimal stream
distribution. We have implemented vSkyConf based on Amazon EC2 and verified the
excellent performance of our design, as compared to the widely adopted unicast
solutions.Comment: 10 page
Dynamic adaptation of streamed real-time E-learning videos over the internet
Even though the e-learning is becoming increasingly popular in the academic environment,
the quality of synchronous e-learning video is still substandard and significant work needs to be
done to improve it. The improvements have to be brought about taking into considerations both:
the network requirements and the psycho- physical aspects of the human visual system.
One of the problems of the synchronous e-learning video is that the head-and-shoulder video
of the instructor is mostly transmitted. This video presentation can be made more interesting by
transmitting shots from different angles and zooms. Unfortunately, the transmission of such
multi-shot videos will increase packet delay, jitter and other artifacts caused by frequent
changes of the scenes. To some extent these problems may be reduced by controlled reduction
of the quality of video so as to minimise uncontrolled corruption of the stream. Hence, there is a
need for controlled streaming of a multi-shot e-learning video in response to the changing
availability of the bandwidth, while utilising the available bandwidth to the maximum.
The quality of transmitted video can be improved by removing the redundant background
data and utilising the available bandwidth for sending high-resolution foreground information.
While a number of schemes exist to identify and remove the background from the foreground,
very few studies exist on the identification and separation of the two based on the understanding
of the human visual system. Research has been carried out to define foreground and background
in the context of e-learning video on the basis of human psychology. The results have been
utilised to propose methods for improving the transmission of e-learning videos.
In order to transmit the video sequence efficiently this research proposes the use of Feed-
Forward Controllers that dynamically characterise the ongoing scene and adjust the streaming
of video based on the availability of the bandwidth. In order to satisfy a number of receivers
connected by varied bandwidth links in a heterogeneous environment, the use of Multi-Layer
Feed-Forward Controller has been researched. This controller dynamically characterises the
complexity (number of Macroblocks per frame) of the ongoing video sequence and combines it
with the knowledge of availability of the bandwidth to various receivers to divide the video
sequence into layers in an optimal way before transmitting it into network.
The Single-layer Feed-Forward Controller inputs the complexity (Spatial Information and
Temporal Information) of the on-going video sequence along with the availability of bandwidth
to a receiver and adjusts the resolution and frame rate of individual scenes to transmit the
sequence optimised to give the most acceptable perceptual quality within the bandwidth
constraints.
The performance of the Feed-Forward Controllers have been evaluated under simulated
conditions and have been found to effectively regulate the streaming of real-time e-learning
videos in order to provide perceptually improved video quality within the constraints of the
available bandwidth
A parallel H.264/SVC encoder for high definition video conferencing
In this paper we present a video encoder specially developed and configured for high definition (HD) video conferencing. This video encoder brings together the following three requirements: H.264/Scalable Video Coding (SVC), parallel encoding on multicore platforms, and parallel-friendly rate control. With the first requirement, a minimum quality of service to every end-user receiver over Internet Protocol networks is guaranteed. With the second one, real-time execution is accomplished and, for this purpose, slice-level parallelism, for the main encoding loop, and block-level parallelism, for the upsampling and interpolation filtering processes, are combined. With the third one, a proper HD video content delivery under certain bit rate and end-to-end delay constraints is ensured. The experimental results prove that the proposed H.264/SVC video encoder is able to operate in real time over a wide range of target bit rates at the expense of reasonable losses in rate-distortion efficiency due to the frame partitioning into slices
Distributed multimedia systems
A distributed multimedia system (DMS) is an integrated communication, computing, and information system that enables the processing, management, delivery, and presentation of synchronized multimedia information with quality-of-service guarantees. Multimedia information may include discrete media data, such as text, data, and images, and continuous media data, such as video and audio. Such a system enhances human communications by exploiting both visual and aural senses and provides the ultimate flexibility in work and entertainment, allowing one to collaborate with remote participants, view movies on demand, access on-line digital libraries from the desktop, and so forth. In this paper, we present a technical survey of a DMS. We give an overview of distributed multimedia systems, examine the fundamental concept of digital media, identify the applications, and survey the important enabling technologies.published_or_final_versio
Supporting real time video over ATM networks
Includes bibliographical references.In this project, we propose and evaluate an approach to delimit and tag such independent video slice at the ATM layer for early discard. This involves the use of a tag cell differentiated from the rest of the data by its PTI value and a modified tag switch to facilitate the selective discarding of affected cells within each video slice as opposed to dropping of cells at random from multiple video frames
Online Reinforcement Learning for Dynamic Multimedia Systems
In our previous work, we proposed a systematic cross-layer framework for
dynamic multimedia systems, which allows each layer to make autonomous and
foresighted decisions that maximize the system's long-term performance, while
meeting the application's real-time delay constraints. The proposed solution
solved the cross-layer optimization offline, under the assumption that the
multimedia system's probabilistic dynamics were known a priori. In practice,
however, these dynamics are unknown a priori and therefore must be learned
online. In this paper, we address this problem by allowing the multimedia
system layers to learn, through repeated interactions with each other, to
autonomously optimize the system's long-term performance at run-time. We
propose two reinforcement learning algorithms for optimizing the system under
different design constraints: the first algorithm solves the cross-layer
optimization in a centralized manner, and the second solves it in a
decentralized manner. We analyze both algorithms in terms of their required
computation, memory, and inter-layer communication overheads. After noting that
the proposed reinforcement learning algorithms learn too slowly, we introduce a
complementary accelerated learning algorithm that exploits partial knowledge
about the system's dynamics in order to dramatically improve the system's
performance. In our experiments, we demonstrate that decentralized learning can
perform as well as centralized learning, while enabling the layers to act
autonomously. Additionally, we show that existing application-independent
reinforcement learning algorithms, and existing myopic learning algorithms
deployed in multimedia systems, perform significantly worse than our proposed
application-aware and foresighted learning methods.Comment: 35 pages, 11 figures, 10 table
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