Omnet++ Research Papers

Recently, live video streaming from hand- held devices entered mass market . Ordinary video conference systems were supplemented with additional features, such as marking in order to point attention of the conference participants on specific object in the video. However, the accuracy of live video marking depends on the video latency, received between two video conference participants.

The aim of this study was to propose a solution to synchronize video object coordinates in two video streams: transmitted and received with latency that is close to 2 seconds. A new system was proposed in this paper designed to track an object in the video stream based on the inertial sensor data. It was found that the displacement of the object of interest during latency interval could be predicted by the use of inertial sensors of the handheld device with 86% accuracy in average.

Heterogeneous cellular networks (HCN) introduce small cells within the transmission range of a macro cell. For the efficient operation of HCNs it is essential that the high-power macro-cell shuts off its transmissions for an appropriate amount of time in order for the low-power small cells to transmit. This is a mechanism that allows time-domain resource partitioning (TDRP) and is critical to be optimized for maximizing the throughput of the complete HCN. In this paper, we investigate video communication in HCNs when TDRP is employed. After defining a detailed system model for video streaming in such an HCN, we consider the problem of maximizing the experienced video quality at all the users, by jointly optimizing the TDRP for the HCN, the rate allocated to each specific user, and the selected video quality transmitted to a user.

The NP-hard problem is solved with a primal-dual approximation algorithm that decomposes the problem into simpler sub problems, making them amenable to fast well-known solution algorithms. Consequently , the calculated solution can be enforced in the time scale of real-life video streaming sessions. This last observation motivates the enhancement of the proposed framework to support video delivery with dynamic adaptive streaming over HTTP (DASH). Our extensive simulation results demonstrate clearly the need for our holistic approach for improving the video quality and playback performance of the video streaming users in HCNs.

There are two types of P2P systems satisfying two different user demands: 1) file downloading and 2)video-on-demand (VoD) streaming. An example of file downloading is the original BitTorrent, and examples for VoD streaming include various commercial P2P-based VoD streaming systems such as that offered by Pp-live. We have a hypothesis – by combining a type: 1) system and 2) system as a single P2P system, both the file downloading users and the streaming users of the same video will benefit in performance. The reasoning is that at any moment, only a subset of the file downloading peers can provide good service to VoD streaming peers and the VoD streaming peers are only good at providing service to a different subset of the file downloading peers.

The former subset is the set of peers close to completing the downloading of the video file; whereas the latter subset is the set of peers starting to download a video. In this paper, we propose a novel design for a mesh-based video distribution system without depending on video replication on streaming peers. We produce simple back-of-the-envelop analysis to show its effectiveness. Then, we further validate our design and compare it with other designs through simulation and experiments in practical networking environment by implementing a prototype.

CISCO VNI predicted an average annual growth rate of 69.1% for mobile video traffic between 2013 and 2018 and accordingly much academic research related to video streaming has been initiated. In video streaming, Adaptive Bit rate (ABR) is a streaming technique in which a source video is stored on a server at variable encoding rates and each streaming user requests the most appropriate video encoding rate from the server considering their channel capacity or signal power. However, these days, ABR related studies are only focusing on real-time rate adaptation and omitting efficiency in terms of energy.

These methods do not consider the energy limited characteristics of mobile devices, which cause dissatisfaction to the streaming users. In this paper, we propose an energy efficient prefetching based dynamic adaptive streaming technique by considering the limited characteristics of the batteries used in mobile devices, in order to reduce the energy waste and provide a similar level of service in terms of the average video rate compared to the latest ABR streaming technique which does not consider the energy consumption.

The H.264 video compression technique enjoys the merit of good video quality with high compression rate; therefore, it has been widely applied in delivering video content. In IEEE 802.11ac wireless local area networks, enhanced distributed channel access (ED-CA) is the primarily adopted access control mechanism for multimedia traffic transmission. However, the performance is degraded due to the unpredictable delay caused by its inefficient back-off procedure. To achieve high quality of service/experience, this paper proposes a multi polling controlled access (MP-CA) scheme to guarantee the latency in delivering important video frames while reducing transmission overhead.

We also propose a cross-layer designed quality adjustment strategy to maximize the visual experience. Furthermore, the down-link multi user multiple-input–multiple-output feature in IEEE 802.11ac is exploited to enhance the reliability of MP-CA. Theoretical analysis is provided to show high efficiency of MP-CA. Simulation results indicate that MP-CA has higher throughput, lower packet delay, lower packet loss, and higher peak signal-to-noise ratio than the ED-CA.

By transmitting texture and depth videos captured from two nearby camera viewpoints, a client can synthesize via depth-image-based rendering (DIBR) any freely chosen intermediate virtual view of the 3D scene, enhancing the user’s perception of depth. During wireless network transmission, burst packet losses can corrupt the transmitted texture and depth videos and degrade the synthesized view quality at the client. In this paper, we propose a multiple description coding system for multi-path transmission of free-viewpoint video, with joint interview and temporal description recovery capability. In particular, we encode separately the even frames of the left view and the odd frames of the right view, and transmit them as one description on one path. The second description comprises the remaining frames in the two views and is transmitted over a second path. If the receiver receives only one description due to burst loss in the other path, the missing frames in the other description are partially reconstructed using our frame recovery procedure.

First, we construct two recovery candidates for each lost pixel in a frame. The first candidate is generated via temporal super-resolution from its predecessor and successor frames in the same view. The second candidate is generated via DIBR from the received frame of the same time instance in the other view. Next, we select the best pixel candidates one patch at a time, where an image patch corresponds to a neighborhood of pixels with similar depth values in the 3D scene. Near-optimal source and channel coding rates for each description are selected using a branch-and-bound method, for given transmission bandwidth on each path. Experimental results show that our system can outperform a traditional single-description/single-path transmission scheme by up to 5.5 dB in Peak Signal-to-Noise Ratio (PSNR) of the synthesized intermediate view at the client.

In this paper, a new method for tunable data hiding in H.264/AVC streams is presented. In order to provide the greatest flexibility for users with varying requirement, a simple control operation for capacity of hiding secret message is proposed.

One group of coefficients chosen from specific macro block is used to accommodate the secret message bits. Based on experimental results, the proposed scheme has been verified that a higher hiding capacity can be obtained at lower degradation on the bit rate and peak signal-to-noise ratio.

Exploring the functional mechanism of the human brain during semantics categorization and subsequently leverage current semantics-oriented multimedia analysis by functional brain imaging have been receiving great attention in recent years. In the field, most of existing studies utilized strictly controlled laboratory paradigms as experimental settings in brain imaging data acquisition. They also face the critical problem of modeling functional brain response from acquired brain imaging data. In this paper, we present a brain decoding study based on sparse multi-nominal logistic regression (SMLR) algorithm to explore the brain regions and functional interactions during semantics categorization. The setups of our study are two folds.

First, we use naturalistic video streams as stimuli in functional magnetic resonance imaging (fMRI) to simulate the complex environment for semantics perception that the human brain has to process in real life. Second, we model brain responses to semantics categorization as functional interactions among large-scale brain networks. Our experimental results show that semantics categorization can be accurately predicted by both intra-subject and inter-subject brain decoding models. The brain responses identified by the decoding model reveal that a wide range of brain regions and functional interactions are recruited during semantics categorization. Especially, the working memory system exhibits significant contributions. Other substantially involved brain systems include emotion, attention, vision and language systems.

This paper describes how optimized streaming strategies, based on MPEG-DASH, can be employed to power a new generation of interactive applications based on immersive video. The latter encompasses ultra-high-resolution, omni-directional and panoramic video. The goal is to deliver experiences that are made up of multiple videos of short duration, which can be joined at run-time in an order defined through user interactions. Applications of the technology are widespread, ranging from virtual walk through to interactive storytelling, the former of which will be featured in detail.

The main technological challenges tackled in this paper are to deliver these experiences in a seamless fashion, at the highest quality level allowed by network conditions and on a wide range of platforms, including the Web. Besides these, the paper focuses on the two-tier software architecture of the proposed framework, as well as a short evaluation to substantiate the validity of the proposed solutions.

Streaming is a delivery of data from server to clients. The data is in form of packets in heterogeneous client environment packet would delay and lost. Multi clients finds its application in wide range of domains which includes 3 G cellular networks and ISM band connect to multi radio clients where clients get confidentiality of connecting with server. This includes detecting the connection of heterogeneous clients in the network. Packet delay happens while streaming of video in multi clients network aims to distort video which effects on quality. Analysis of packets delay or packet loss while streaming from server to client communicated between the nodes and works fine if every packets.

The proposed system targets to disclose the distortion of anonymous frames in a W-LAN network where Heuristic Algorithm is employed for packet loss and packet delay detection. The proposed work will find out the packet delay and packet loss of a frame for the analyzed video. The results can be obtained in windows form where each server windows associated with delivery and clients represents connection and packet delay or loss is reduced. The work completed up till now includes access network connection in static manner and analyzing video when it is completely deliver to client.