About Omnet Project Team

Video content currently makes up nearly half of the “fixed” Internet traffic and more than a third of the mobile traffic in North America, with most other regions showing similar trends. As mobile data rates continue to increase and more people rely on 802.11 wireless for home and commercial Internet access, the amount of video transmitted over at least one wireless hop will likely continue to increase.

In addition, as cameras continue to become smaller and cheaper, the demand for video services in sensor and MANET networks will also increase. In this paper, we examine the state of the art of wireless video communication at each layer of the networking stack. We consider both existing and emerging technologies at each layer of the protocol stack as well as cross-layer designs, and discuss how these solutions can increase the video experience for the end user.

Online micro blogging has been very popular in today’s Internet, where users follow other people they are interested in and exchange information between themselves. Among these exchanges, video links are a representative type on a micro blogging site. The impact is fundamental-not only are viewers in a video service directly coming from the micro blog sharing and recommendation, but also are the users in the micro blogging site representing a promising sample to all the viewers. It is intriguing to study a proactive service deployment for such videos, using the propagation patterns of micro blogs.

Based on extensive traces from Youku and Tencent Weibo, a popular video sharing site and a favored micro blogging system, we explore how video propagation patterns in the micro blogging system are correlated with video popularity on the video sharing site. Using influential factors summarized from the measurement studies, we further design a neural network-based learning framework to predict the number of potential viewers and their geographic distribution. We then design proactive video deployment algorithms based on the prediction framework, which not only determines the upload capacities of servers in different regions, but also strategically replicates videos to these regions to serve users. Our Planet Lab-based experiments verify the effectiveness of our design.

Optimizing cloud gaming experience is no easy task due to the complex trade-off between gamer quality of experience (QoE) and provider net profit. We tackle the challenge and study an optimization problem to maximize the cloud gaming provider’s total profit while achieving just-good-enough QoE. We conduct measurement studies to derive the QoE and performance models. We formulate and optimally solve the problem. The optimization problem has exponential running time, and we develop an efficient heuristic algorithm. We also present an alternative formulation and algorithms for closed cloud gaming services with dedicated infrastructures, where the profit is not a concern and overall gaming QoE needs to be maximized.

We present a prototype system and test bed using off-the-shelf virtualization software, to demonstrate the practicality and efficiency of our algorithms. Our experience on realizing the test bed sheds some lights on how cloud gaming providers may build up their own profitable services. Last, we conduct extensive trace-driven simulations to evaluate our proposed algorithms. The simulation results show that the proposed heuristic algorithms: (i) produce close-to-optimal solutions, (ii) scale to large cloud gaming services with 20,000 servers and 40,000 gamers, and (iii) outperform the state-of-the-art placement heuristic, e.g., by up to 3.5 times in terms of net profits.

This paper presents a novel smart down-link scheduling scheme to enhance the performance of multimedia transmission over LTE (Long Term Evolution) networks. LTE represents a promising framework for next generation broadband multimedia services because of its significantly increased data rate over 3G cellular networks. However, the current LTE scheduling scheme has been designed largely for general data traffic without adequate consideration of multimedia characteristics. Moreover, the “hard” hand-off (HO) procedure adopted in LTE will further degrade the multimedia services when the mobile user moves from one cell to another. Even with increased data rate, current LTE systems still cannot meet the expected quality-of-service (QoS) to the mobile users under various mobility scenarios, especially when the hard HO is evoked. Aiming at overcoming these major challenges, we develop in this research a QoS-driven smart down-link scheduling scheme for enhanced multimedia transmission over LTE network.

The proposed design shall consider the following QoS metrics: 1) The delay constraint of voice-over-IP (VoIP) flow; 2) The packet deadline of video flow; 3) The service degradation induced by hard HO procedure. We achieve the design objectives by creating three QoS driven operational control modules: a transmission delay control module to ensure the on-time arrival of various types of multimedia data, a HO control module to warrant continuous multimedia services when the user moves across a cell boundary, and a resource allocation module to strategically map the requested flows to best-fit radio resource blocks. Simulation results confirm the performance gain of the proposed scheme.

Mobile devices are increasingly used as terminals for playback of multimedia content. However, maximizing the user’s quality of experience is challenging due to the highly variable conditions of the wireless channels. A possibility to cope with such a variability is to dynamically adapt the source coding rate during the transmission, which is the underlying idea of the DASH standard. This work proposes a new framework to improve the quality of the DASH-based streaming experience by allowing to adjust the trade-off between the quality of received content and the risk of playback freeze due to an empty buffer, which is a strong quality-disruptive event.

The problem is analytically formulated and an efficient method to compute the playback freeze probability as a function of the representation choices over time is presented. Numerous simulation results using real download rate traces of 3G channels show the performance improvement compared to other bandwidth-adaptive algorithms as well as the robustness of the framework to variations of its most important parameters.

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.