About Omnet Project Team

In this paper, we shall consider the problem of deploying attention to the subsets of the video streamsfor collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer’s attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream.

The approach proposed here is suitable to be exploited for multi-stream videosummarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g., activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Data Set, a publicly available data set, are presented to illustrate the utility of the proposed technique.

Video streaming can drain a smartphone battery quickly. A large part of the energy consumed goes to wireless communication. In this article, we first study the energy efficiency of different video content delivery strategies used by service providers and identify a number of sources of energy inefficiency. Specifically, we find a fundamental tradeoff in energy waste between prefetching small and large chunks of video content: small chunks are bad because each download causes a fixed tail energy to be spent regardless of the amount of content downloaded, whereas large chunks increase the risk of downloading data that user will never view because of abandoning the video.

Hence, the key to optimal strategy lies in the ability to predict when the user might abandon viewing prematurely. We then propose an algorithm called eSchedule that uses viewing statistics to predict viewer behavior and computes an energy optimal download strategy for a given mobile client. The algorithm also includes a mechanism for explicit control of traffic overhead, i.e., unnecessary download of content that the user will never watch. Our evaluation results suggest that the algorithm can cut the energy waste down to less than half compared to other strategies. We also present and experiment with an Android prototype that integrates eSchedule into a YouTube downloader.

Cognitive radio (CR) is a promising technology to alleviate spectrum shortage and satisfy the huge demand of bandwidth for multimedia streaming in future mobile computing systems. The inherent features of CR pose tough challenges in provisioning quality of service (QoS) for acceptable user experience and minimizing energy consumption for multimedia transmissions. In this paper, scalablevideo coding and transmission rate adaptation are jointly considered in an energy-efficient scheme for transmissions of streaming media over CR with QoS guarantee.

An event-driven discrete-time Markov control process model is introduced to formulate the QoS-guaranteed energy-efficient transmission problem as a constrained stochastic optimization problem. Based on estimations of potentials and the difference between performance measurement and QoS requirement, an online policy iteration algorithm is proposed to optimize energy consumption under QoS constraints directly. By exploiting the system dynamics, this algorithm does not depend on any prior knowledge of channel availability or fading statistics, and it can converge to a near optimum with a low computational burden. Simulation results demonstrate the effectiveness of the proposed method.

In this paper, we consider queue-aware beamforming control for video streaming applications in multiantenna interference network. Using heavy traffic approach, we derive the diffusion limit for the discrete time queuing system and obtain an ergodic excess beamforming control problem for the diffusion limit system. The ergodic control problem is to minimize the average power costs of the access points subject to the constraints on the playback interruption costs and buffer overflow costs of the mobile users. To deal with the queue coupling challenge, we utilize the weak interference coupling property in the network.

Using the theories of calculus and perturbation techniques, we derive a closed-form approximate priority function of the optimality equation and the associated error bound. Based on this approximation, we propose a low complexity queue-aware beamforming control algorithm, which is asymptotically optimal for sufficiently small cross-channel path gain. Finally, the proposed scheme is compared with various baselines through simulations and it is shown that significant performance gain can be achieved.

User experience about the provisioning of a service over LTE mobile networks has become a crucial aspect for Mobile Network Operators. Monitoring network performances may not be sufficient, because they have to be correlated to the specific service experienced by the user. In order to do this, it is important to model network architecture in relation to the service.

For this reason, in this paper, several LTE functional models have been proposed, for real time services like VoLTE and Video Streaming with MNO’s CDN and OTT, in which the standardized LTE architecture is strictly modeled on the service offered to the user. The main contribution of this research relies on the possibility for the MNO to adopt the 4G architectural LTE Video Streaming and VoLTE network, based on the respectives functional model, to conduct the accurate QoE assessment.

In a multicast video streaming service the same multimedia content is sent to a mass audience using only one multicast stream. In multicast video streaming over a cellular network, due to the nature of the multicast communication, from a source to multiple recipients, and due to the characteristics of the radio channel, different for each receiver, transmission errors are addressed at the application level by using Forward Error Correction (FEC) techniques.

However, in order to protect the communication over the radio channel, FEC techniques are also applied at the physical layer. Another important technique to improve the communication of the radio channel is the use of a single-frequency network. This paper analyzes the performance of a video streaming service over a cellular network taking into account the combined impact of different factors that affect the transmission, both the physical deployment of the service and the two levels of FEC.

We consider the video-on-demand streaming problem in a P2P network in a decentralized model, in which peers have no global information about the network. Assuming that only one server has all the chunks, the objective is to stream all chunks to all peers in the network such that small latency and good fluency are achieved by all peers.

We design a simple and decentralized protocol in which each peer maintains a constant number of neighbors and only need to communicate with one of them chosen uniformly at random every time. Moreover, the maximum number of communications established on each peer every time is also constant. We provide theoretical and experimental analysis to show that almost all peers achieve optimal latency and fluency under our protocol.

This paper presents a peer-to-peer (P2P) framework for the deployment of live video streaming applications over P2P overlay networks.

The proposed framework provides support for flash-crowds, decentralizes decision making and makes use of network coding to reduce bandwidth consumption. We present the framework and the simulation results that demonstrate the framework’s effectiveness.

We propose a novel robot control system by transmitting robot control information on existing videostreaming services as dynamic encoded two-dimentional visual code. We implemented sensor data transmitting system by using dynamic encoded two-dimentional visual code which called SENSe-TREAM [1] and we built the robot controlling system by using SENSeTREAM architecture.

This paper shows the architecture of robot controlling system and future vision of telepresence and human-robot interaction.

Scalable High Efficiency Video Coding (SHVC), a new ISO/ITU video compression Standard is an ideal candidate for delivering Ultra High Definition (UHD) or 4k resolution content to the rapidly growing high resolution clients, especially when there is a mix of clients of SD, HD and UHD resolutions. This is especially true for video streaming in overlay or over-the-top (OTT) networks using HTTP or adaptive bitrate streaming technologies. SHVC adds scalability layers on top of the ultra-efficient HEVC widely considered to be twice as good as the currently widely deployed Advanced Video Coding (AVC or H.264) Standard.

In addition, SHVC promises scalability through lightweight (mostly syntactical) additions to HEVC. This paper investigates both the savings and the overhead of encoding UHD content as an SHVC enhancement layer with AVC or HEVC HD base layer, primarily focusing on the spatial scalability aspect of SHVC. Also, we briefly investigate adopting existing rate allocation technique for picking optimal bitrates for base and enhancement layers for a given distribution of HD and UHD clients.