About Omnet Project Team

This work proposes a no-reference video quality metric that considers two parameters, pauses and changes in video resolution. Results indicate that users’ Quality-of-Experience (QoE) is highly correlated with these parameters.

The proposed metric has low complexity because it is based on application-level parameters; it can, therefore, be easily implemented in consumer electronic devices.

The influence of color spaces and histogram distances on object re-identification in video stream is investigated.

Dataset with images of pedestrians is being analyzed. A set of color spaces, histograms types and distance functions is applied to minimize re-identification errors by calculating false acceptance and false rejection error rates.

An Autonomous Mobile Robot is used to travel from one source to destination using two or more wheels. In order to reach a target where the person could not reach that particular destination, they must depend on some non-human called Mobile Robots. The Proposed work contains Autonomous Arduino Yun four wheeled mobile robot is allowed to move from source to destination position for finding a path, avoiding the obstacles and Video Streaming.

These are achieved using a Wi-Fi technology, by avoiding the existing work with Bluetooth technology which is less secure and covers only shorter range. The best path is identified by using an Aggrandized Genetic Algorithm (AGA) which is best compared to few other algorithms. In order to achieve a secure communication and travelling to a longer distance when critical situation occurs to the person for reaching a destination from source, the Wi-Fi (IEEE 802.11 b/g/n) is useful for reaching the task.

The rapid growth of video in wireless networks is a crucial issue to be addressed by content providers. Nowadays, an emerging and promising trend is the development of solutions aimed at maximizing the quality of experience (QoE) of end users. However, the prediction of the QoE perceived by users in different conditions remains a major challenge. In this paper, we propose a two-layer hierarchical QoE model for wireless video streaming systems that employ fountain codes in forward error correction (FEC).

The first layer decomposes the impact of fountain codes on the user QoE into three factors, namely the initial playback delay, the rebuffering frequency and the rebuffering time. The second layer of the QoE model relates the user QoE in terms of the mean opinion score (MOS) to the aforementioned three factors using a quadratic function. An exemplary application of the developed QoE model in the admission control of a bandwidth-limited video on demand (VoD) system is given with the goal toward guaranteeing the QoE quality of all users. Simulation results confirms the effectiveness of the developed admission control technique.

Information Centric Networking (ICN) aims to evolve the Internet from a host-centric to a data-centric paradigm. In particular, it improves performance and resource efficiency in events with large crowds where many users in a local area want to generate and watch media content related to the event. In this paper, we present the design of a live video streaming system built on the NetInf ICN architecture and how the architecture was adapted to support live streaming of media content.

To evaluate the feasibility and performance of the system, extensive field tests were carried out over several days during a major sports event. We show that our system streams videos successfully with low delay and communication overhead compared with existing Internet streaming services, by scalability tests using emulated clients we also show that it could support several thousands of simultaneous users.

This paper proposes novel Raptor-aware link adaptation (LA) when application layer Forward Error Correction (AL-FEC) with Raptor codes is used for live, high quality, video unicast over mobile broadband networks. The use of Raptor code AL-FEC is taken into account for the adaptation of the modulation and coding scheme (MCS) used in the physical layer. A cross-layer optimization approach is used to select the Raptor code parameters and the MCS mode jointly, in order to maximize transmission efficiency.

The proposed methodology takes into consideration the channel resources required to accommodate the Raptor overheads. Simulation results show that packet loss is eliminated and the amount of radio resource required is reduced significantly. Automatic repeat request (ARQ) based unicast systems require up to 115.6 percent more channel resources, by comparison to the proposed Raptor-aware LA system without retransmissions. Furthermore, the Raptor-aware LA system can enhance the link budget by up to 4 dB, increasing coverage in LoS locations, and can improve total goodput by 46.7 percent compared to an ARQ-based system.

Cloud computing have one technology development that favor various computing services and storage over the Internet. The multimedia services are examples of these new services will now use this new technology. About the multimedia services, we highlight the streaming video service, where the user can access your videos from these cloud environments with the same quality practiced in traditional environments.

In this paper, we used the models to find the RBD availability of streaming videoservices in a test environment assembled in the laboratory to evaluate the system. Sensitivity analysis was used to identify the critical points of the system with respect to availability so that you can act on them and thus improve the system availability.

Real-time video dissemination over Vehicular Ad hoc Networks (VANETs) is fundamental for many services, e.g., emergency video delivery, road-side video surveillance, and advertisement broadcasting. These applications deal with several challenges due to strict video quality level requirements and highly dynamic topologies. To handle these challenges, geographic receiver-based beacon-less approaches have been proposed as a suitable solution for forwarding video flows in VANETs. In general, the routing decisions are performed only based on network, link, and/or node characteristics, such as link quality and vehicle’s location.

However, in real situations, due to different requirements and hierarchical structures of multimedia applications, these existent routing decisions are not satisfactory to select the best relay nodes and build up reliable backbones to delivery video content with reduced delay and high Quality of Experience (QoE). This paper introduces the QOe-Driven and LInk-qualiTy rEceiver-based (QOALITE) protocol to allow live video dissemination with QoE assurance in Vehicle-to-Vehicle (V2V) scenarios. QOALITE considers video and QoE-awareness, coupled with location and link quality attributes for relay selection. Simulation results show the benefits of QOALITE when compared to existing work, while achieving multimedia transmission with QoE support and robustness in highway scenarios.

In the multiview video plus depth (MVD) coding format, both texture and depth views are jointly compressed to represent the 3-D video content. The MVD format enables synthesis of virtual views through depth-image-based rendering; hence, distortion in the texture and depth views affects the quality of the synthesized virtual views. Bit allocation between texture and depth views has been studied with some promising results. However, to the best of our knowledge, most of the existing bit-allocation methods attempt to allocate a fixed amount of total bit rate between texture and depth views; that is, to select appropriate pair of quantization parameters for texture and depth views to maximize the synthesized view quality subject to a fixed total bit rate. In this paper we propose a scalable bit-allocation scheme, where a single ordering of texture and depth packets is derived and used to obtain optimal bit allocation between texture and depth views for any total target rates.

In the proposed scheme, both texture and depth views are encoded using the quality scalable coding method; that is, medium grain scalable (MGS) coding of the Scalable Video Coding (SVC) extension of the AdvancedVideo Coding (H.264/AVC) standard. For varying target total bit rates, optimal bit truncation points for both texture and depth views can be obtained using the proposed scheme. Moreover, we propose to order the enhancement layer packets of the H.264/SVC MGS encoded depth view according to their contribution to the reduction of the synthesized view distortion. On one hand, this improves the depth view packet ordering when considered the rate-distortion performance of synthesized views, which is demonstrated by the experimental results. On the other hand, the information obtained in this step is used to facilitate optimal bit allocation between texture and depth views. Experimental results demonstrate the effectiveness of the proposed scalable bit-allocation scheme for texture and depth view- .

With the proliferation of wireless cellular technology (e.g., 5G, 4G LTE), managing quality of experience (QoE) for wireless clients streaming different videos over these networks is becoming increasingly important. In this paper, we consider the problem of managing playout stalls experienced by multiple clients streaming different videos from a cellular base station. We build on our prior work [1], where we formulate an epoch based lead aware multiple video transmission (LMVT) problem to minimize the total number of playout stalls experienced by mobile clients.

In [1], [2], we show that the LMVT problem is NP-hard and propose a pseudo polynomial dynamic programming solution and a simple greedy heuristic to solve the problem. In this paper, we first show that even a simpler and less restrictive version of the LMVT problem remains hard. We then design a fully polynomial time approximation scheme (FPTAS) for the LVMT problem by demonstrating that it is equivalent to the Santa Claus Problem [3], [4]. The FPTAS provides a bounded performance guarantee, differing by a factor of 1/1+ϵB from the optimal solution (where B is the number of time slots in an epoch and ε is a small constant).