About Omnet Project Team

In cloud gaming systems, the game program runs at servers in the cloud, while clients access game services by sending input events to the servers and receiving game scenes via video streaming. In this paradigm, servers are responsible for all performance-intensive operations, and thus suffer from poor scalability. An alternative paradigm is called graphics streaming, in which graphics commands and data are offloaded to the clients for local rendering, thereby mitigating the server’s burden and allowing more concurrent game sessions. Unfortunately, this approach is bandwidth-consuming, due to large amounts of graphic commands and geometry data.

In this paper, we present Live Render, an open-source gaming system that remedies the problem by implementing a suite of bandwidth optimization techniques including intraframe compression, inter-frame compression, and caching, establishing what we call compressed graphics streaming. Experiments results show that the new approach is able to reduce bandwidth consumption by 52%–73% compared to raw graphics streaming, with no perceptible difference in video quality and reduced response delay. Compared to the video streaming approach, Live Render achieves a traffic reduction of 40%–90% with even improved video quality and substantially smaller response delay, while enabling higher concurrency at the server.

Video source identification is very important in validating video evidence, tracking down video piracy crimes, and regulating individual video sources. With the prevalence of wireless communication, wireless video cameras continue to replace their wired counterparts in security/surveillance systems and tactical networks. However, wirelessly streamed videos usually suffer from blocking and blurring due to inevitable packet loss in wireless transmissions. The existing source identification methods experience significant performance degradation or even fail to work when identifying videos with blocking and blurring.

In this paper, we propose a method that is effective and efficient in identifying such wirelessly streamed videos. In addition, we also propose to incorporate wireless channel signatures and selective frame processing into source identification, which significantly improve the identification speed. We conduct extensive real-world experiments to validate our method. The results show that the source identification accuracy of the proposed scheme largely outperforms the existing methods in the presence of video blocking and blurring. Moreover, our method is able to identify the video source in a near-real-time fashion, which can be used to detect the wireless camera spoofing attack.

In IEEE 802.11 wireless local area network (WLAN), the quality of the experience (QoE) of HTTP video streaming is seriously influenced by the restricted bandwidth and shared resources. However, to the best of our knowledge, the assessment and optimization of QoE of HTTP video streaming in WLAN have not been fully studied yet. In this paper, we propose an objective metric to assess the QoE and obtain the relationship between this metric and the achieved MAC layer throughput, which makes the MAC layer be aware of the QoE performance directly.

Then, a novel admission control and MAC layer parameter configuration algorithm is proposed, which optimizes the QoE of HTTP video streaming and guarantees the throughput requirement of background traffic. Simulation results show that the proposed algorithm outperforms legacy IEEE 802.11 DCF in QoE optimization by a factor of six or more when the number of background stations is large.

Software defined networking (SDN) makes networks programmable and application-aware by decoupling network control and management (NC&M) from data forwarding and leveraging centralized NC&M to facilitate user-customized routing and switching. Inspired by these, this paper investigates how to realize the Open Flow-controlled (OF-controlled) network orchestration that can facilitate efficient scalable video coding (SVC) streaming to heterogeneous clients. Specifically, we consider real-time SVC streaming and address the situation in which video sources reside in geographically- distributed servers and clients can join and leave the streaming services dynamically. We formulate this as a multi-source multi-destination many cast problem and realize the networking system with an OF-controlled SDN architecture.

We first design the OF controller to enable efficient network operations. Then, we focus on solving the multi-source multi-destination SVC video many cast problem and design several algorithms. Initially, an integer linear programming (ILP) model is formulated to obtain the optimal solutions for small-scale problems. Next, we try to make the many cast algorithm suitable for practical implementation, and design two time-efficient heuristics. Simulation results indicate that the heuristics can provide close-to-optimal solutions. Finally, we build an OF network test bed that consists of OF switches, SVC video servers and clients, and perform SVC streaming experiments to demonstrate our design. Experimental results verify that the proposed scheme can allocate bandwidth intelligently and ensure high-quality video streaming. To the best of our knowledge, this is the first work that accomplishes experimental demonstration of OF-controlled network orchestration for adaptive SVC video many cast.

Dynamic Adaptive Streaming over HTTP (DASH) is slowly becoming the most popular online video streaming technology. DASH enables the video player to adapt the quality of the multimedia content being downloaded in order to match the varying network conditions. The key challenge with DASH is to decide the optimal video quality for the next video segment under the current network conditions. The aim is to download the next segment before the player experiences buffer-starvation. Several rate adaptation methodologies proposed so far rely on the TCP throughput measurements and the current buffer occupancy. However, these techniques, do not consider any information regarding the next segment that is to be downloaded.

They assume that the segment sizes are uniform and assign equal weights to all the segments. However, due to the video encoding techniques employed, different segments of the video with equal playback duration are found to be of different sizes. In the current paper, we propose to list the individual segment characteristics in the Media Presentation Description (MPD) file during the preprocessing stage; this is later used in the segment download time estimations. We also propose a novel rate adaptation methodology that uses the individual segment sizes in addition to the measured TCP throughput and the buffer occupancy estimate for the best video rate to be used for the next segments.

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.