Omnet++ Research Papers

With increasing number of TV channels and growing need for on-demand services, the traditional digital terrestrial television (DTT) is becoming a less attractive way of distributing TV contents. As an alternative, we discuss a converged platform in UHF band for TV and mobile broadband provisioning based on LTE cellular technology and infrastructure, here referred to as Cell TV. The requirement for Cell TV is to provide a seamless TV coverage from urban to rural environments and to minimize the spectrum requirement so that the leftover can be used for mobile services. We formulate an optimal spectrum allocation problem for Cell TV to distribute different TV channels with different transmission modes.

Each TV channel is delivered via either uni cast links or broadcast over single frequency networks (SFNs) of different modulation orders according to the location-dependent viewing demand and cellular infrastructure availability. Based on a case study of the Greater Stockholm region, we identify that Cell TV requires only a small portion of the UHF band to deliver the TV contents in urban areas, thus releasing a significant amount of spectrum for mobile broadband services. Meanwhile, the spectrum requirement for Cell TV is considerably higher in suburban and rural areas due to the transitions of transmission modes. We further generalize these findings to provide a guiding principle for Cell TV deployment in mixed environments and also to demonstrate the flexibility advantage of Cell TV in adapting to the growing diversity of TV contents.

To maximize video playback time of smartphones, both data usage and energy consumption need to be considered simultaneously because users may stop playing the video when either available data quota or battery energy is about to deplete. In this letter, we propose an algorithm determining an optimal operating parameter to selectively save more depleting resource.

For this purpose, we numerically analyze how HTTP-based video services affect the data usage and energy consumption. Then, we show that the proposed scheme effectively extends the playback time compared with other schemes that only consider either data usage or energy consumption.

Recent years have witnessed a significant rise in the number, duration and variety of video contents, which contribute to the bulk of internet traffic. With increase in smartphone and tablet users, watching videos on mobile devices has become one of its most popular use cases. These devices live on limited battery energy which is still a major bottleneck and a source of user dissatisfaction during video playback. In this paper we introduce an intermediate framework called VIDalizer for power efficient video delivery to smartphones and tablets.

This almost transparent to the user, battery aware framework takes away some of the video processing overhead from the device and intelligently tunes its parameters customized for the mobile device while delivering the video using a novel transport protocol. Our preliminary results show that this framework can significantly reduce energy consumption up to 45%-55% of a mobile device without compromising user experience.

We present the Information-Centric Access Network (I-CAN) architecture, which is based on the publish-subscribe Information-Centric Networking (ICN) paradigm, identifying how it accounts for specific characteristics of mobile and wireless access networks.

We also present initial results from the test-bed implementation of two application scenarios that exploit key features of the I-CAN architecture: secure publication proxy and multi-source mobile video streaming.

As video streaming becomes one of the most fast growing and dominant applications in fixed and mobile networks, how to provide high quality and user satisfaction is a widely studied research topic. In this paper, we develop an analytical framework to derive the downloading rate and bandwidth requirement, so that certain objective quality of experience (QoE) constraints are met.

Particularly, application-specific key performance indicators (KPIs) such as start-up delay and starvation probability are taken into account. Our analysis addresses heterogeneity of both user spatial locations and videorequests. Computer simulations are conducted to verify the accuracy of the proposed analytical framework. Based on the analytical framework, a media server can adapt the downloading rate allocation, e.g., relative to the video playback rate, depending on user demands and network conditions.

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.