PERFORMANCE ANALYSIS OF FORWARD ERROR CORRECTION AIDED P2P LIVE-VIDEO STREAMING

In this paper, we address the problem of QoS (Quality of Service) provisioning for multi-user video streaming over multiple heterogeneous wireless networks based on the distributed, cross-layer design framework. By jointly considering the rate allocation and the Joint Source-Channel Coding (JSCC), our proposal aims at maximizing the QoS provisioning under the given resource constraint. At first, we develop and evaluate a framework for optimal video rate allocation over multiple networks based on the observed Available Bit Rate (ABR) and the Round Trip Time (RTT) over each access network, as well as the video rate-distortion characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the sum of all video streams expected distortion. Then, we propose an analytical JSCC scheme for error-resilient scalable encoded video, and integrate the JSCC with the specific rate allocation algorithm to improve the constructed video quality by optimally applying the appropriate channel coding rate given the constraints imposed by the transmission rate and the prevailing channel conditions. Objective and subjective simulation results are provided which demonstrate the effectiveness of our proposed joint scheme.

Internet-based video streaming has significantly picked up in the market place, followed by almost a decade of intensive research and standard efforts. Not only are we interested in the current status of video streaming, but also the future that it is heading towards. The paper discusses scalable server and storage architectures for video streaming.

Live Road Surveillance video streaming is a new type of safety data that can be transmitted in Vehicular Ad Hoc Networks (VANETs). Video streaming is a high-bandwidth-demanded service and causes congestion if there are many vehicles that stream video in the wireless network at the same time. Clustering is an efficient technique to relieve network congestion because it can reduce the number of vehicles to transmit data. Based on the effective-vision of the camera in vehicles, an Effective-Vision-Area-based Clustering (EVAC) algorithm for LRS service is proposed in this paper. EVAC can construct clusters such that a driver X can join a suitable cluster to get the LRS service from the appropriate vehicle ahead of X to have additional vision. In this paper, we compare the proposed EVAC algorithm for LRS service with other LRS service provider selection schemes that do not adopt the clustering mechanism in term of different performance metrics to show the effectiveness of the proposed work.

We demonstrate experimentally for the first time the impact of forward error correction (FEC) on the performance of 622/1244 Mb/s burst-mode clock and data recovery (BM-CDR) with instantaneous phase acquisition (0 bit) for any phase step (plusmn27pi rads) for gigabit-capable passive optical network (GPON) optical line terminator (OLT) applications with (255, 239) Reed-Solomon (R-S) codes. Our design is based on commercially available SONET CDRs operated in 2times over sampling mode. This burst-mode receiver (BM-RX) provides a ~5 dB coding gain at bit error ratio (BER) of 10-10. We also show that this novel technique of employing FEC on BM-CDRs with fast phase acquisition time, provides a solution for fast burst-error correction giving reliable and predictable BERs in bursty-channels. The BM-RX meets the GPON physical media dependent layer specifications defined in the ITU-T G.984.2 recommendation. The coding gain can be used to increase the optical link budget as specified in the ITU-T G.984.3 standard, that is, support higher bit rates, achieve longer physical reach between the OLT and the optical network units (ONUs), as well as increase the number of splits per single PON tree.

We investigate the impact of Forward Error Correction (FEC) and retransmissions on the video quality of packetized HDTV video flows with possible buffer overflow at the ingress node of the video network. The use of protection is a mixed blessing. On the one hand, it increases the packet rate associated with each video flow (due to overhead), which results in a higher buffer overflow probability. On the other hand, protection allows for the recovery of lost packets, which alleviates the problem of buffer overflow. It turns out that the overall impact of FEC and retransmission is very beneficial for the video quality. Moreover, retransmissions clearly outperform FEC, and can ensure HDTV video quality up to loads at the ingress node close to one.

This paper proposes an audiovisual 3D multimedia system based on multi-view video and wave field synthesis cooperated with the object-based audio. The spatial audio rendering method based on wave field synthesis is particularly useful for applications where multiple users experience true immersive sound while being free to move without losing spatial sound effects. The unique features of the methodology can be very useful in high quality virtual experience applications, and in particular for creating precise audio objects synchronised with multi-view video stream, delivered to the users in network platforms. The paper introduces a novel 3D multimedia rendering and streaming architecture over the Internet, and also reports on the experimental results. The multimedia system is demonstrated and con-firmed that while covering the viewing angle, the developed approach can create a variety of virtual audio objects at target positions with very high accuracy.

We report the implementation experience and experimental evaluation of a staggered adaptive forward error correction (FEC) system for video multicast over wireless LANs. In the system, the parity packets generated by a cross-packet FEC code are transmitted at a time delay from the original video packets, i.e. staggercasting video stream and FEC stream in different multicast groups. The delay provides temporal diversity to improve the robustness of video multicast, especially to enable the clients to correct burst packet loss using FEC and to achieve seamless handoff. A wireless client dynamically joins the FEC multicast groups based upon its channel conditions and handoff events. We have implemented the system including the streaming server and client proxy. A novel software architecture is designed to integrate the FEC functionality in the clients without requirement for changing the existing video player software. We conduct extensive experiments to investigate the impact of FEC overhead and the delay between the video stream and FEC stream to the video quality under different interference levels and mobile handoff durations. The efficacy of staggered adaptive FEC system on improving video multicast quality is demonstrated in real system implementation.

Traditional Forward Error Correction (FEC) mechanisms can be divided into Packet level FEC (PFEC) mechanisms and Byte level FEC (BFEC) mechanisms. The PFEC mechanism of recovering from errors in a source packet requires an entire FEC redundant packet even though the error involves a few bit errors. The recovery capability of the BFEC mechanism is only half of the FEC redundancy. Accordingly, an adaptive Sub-Packet FEC (SPFEC) mechanism is proposed in this paper to improve the quality of video streaming data over wireless networks, simultaneously enhancing the recovery performance and reducing the end-to-end delay jitter. The SPFEC mechanism divides a packet into n sub-packets by means of the concept of a virtual packet. The SPFEC mechanism uses a checksum in each sub-packet to identify the position of the error sub-packet. Simulation experiments show the adaptive SPFEC mechanism achieves high recovery performance and low end-to-end delay jitter. The SPFEC mechanism outperforms traditional FEC mechanism in terms of packet loss rate and video Peak Signal-to-Noise Ratio (PSNR). SPFEC offers an alternative for improved efficiency video streaming that will be of interest to the designers of the next generation environments.

Most students assume that mathematics is a complex subject and has a higher level of difficulty in its teaching. The subject of this study is the seventh grade students of Darunnajah Wuluhan Jember Middle School. Based on the existing problems in this study, a problem was formulated, namely how the process of implementing RME based on Video Streaming in Grade VII students of Darunnajah Wuluhan Middle School and whether RME based on Video Streaming could improve students’ learning outcomes. The results revealed that the students’ learning outcomes respective in Cycle I was 70.05% and Cycle II was 94.1%. Thus, it shows an increase of 11.8% in mathematics learning. It can be concluded that RME based on Video Streaming can improve students’ learning outcomes. Keywords: RME, Video Streaming

In the Internet Protocol (IP) ecosystem, Quality-of-Experience (QoE) is important information needed by Service Providers (SP) to improve their services. However, end-user's satisfaction, which can be reflected by QoE metrics, cannot be easily measured like technical variables, such as bandwidth and latency. QoE can either be estimated through mathematical models or it can be measured through an experimental setup. In this work a Voice-over-Internet Protocol-based (VoIP) QoE measurement setup has been designed to capture end-user's QoE in VoIP services. The data measured during these experiments are used to define all necessary parameters of the Deterministic QoE model (DQX) in this VoIP scenario. Such a calibration of the model is essential to adapt it to the particular service and its technical and non-technical conditions in which it is used. Furthermore, those DQX results achieved are compared with those results of the IQX Hypothesis and the E-Model, being proposed by the ITU-T. Thus, it is finally shown that DQX can capture more accurately end-user's QoE in VoIP scenarios.

The proliferation problem of video streaming applications and mobile devices has prompted wireless network operators to put more efforts into improving quality of experience (QoE) while saving resources that are needed for high transmission rate and large size of video streaming. To deal with this problem, we propose an energy-aware rate and description allocation optimization method for video streaming in cellular network assisted device-to-device (D2D) communications. In particular, we allocate the optimal bit rate to each layer of video segments and packetize the segments into multiple descriptions with embedded forward error correction (FEC) for realtime streaming without retransmission. Simultaneously, the optimal number of descriptions is allocated to each D2D helper for transmission. The two allocation processes are done according to the access rate of segments, channel state information (CSI) of D2D requester, and remaining energy of helpers, to gain the highest optimization performance. Simulation results demonstrate that our proposed method (named OPT) significantly enhances the performance of video streaming in terms of high QoE and energy saving.

The advent of Cyber-Physical Systems (CPS) has brought a revolutionary change coined as a mixture of information, communication, computation, and control. With applications in smart grid, health monitoring, automatic avionics, distributed robotics, etc., CPS is currently an area of attention among the academia and industry. The advancement of mobile communications and embedded technology has made it possible to build large scale CPS consisting of the interconnection of mobile phones. These devices collect information about the surrounding environment at any time anywhere basis through real-time video capture. Video streaming has proven to be a massive industry that is growing rapidly playing an important role in everyday life. Customer-driven approach wanting best experience with quality has to be the core offering of contemporary scenario. Video streaming is categorized into Video-On-Demand Streaming (VoDS) and Live Video Streaming (LVS) showing the current state-of-art opportunities. Many diverse applications of video streaming are military video surveillance using drones, live sports match player face recognition, on-demand video characters recognition, movie summarization like identifying parts of the movie which are viewed many times by different users, movie and series recognition, motion detection, gesture recognition, image segmentation, etc. This paper introduces an approach to develop video analysis on VoD and LVS using cloud-based services and analyzes the impact of Quality of Experience (QoE), cost, and bandwidth on the cloud. To achieve the best user experience for video streaming and video analysis, Content Delivery Network (CDN) offers the best QoE at various analyzed locations using various cloud providers like Amazon Web Services (AWS) CloudFront, Google Cloud CDN, Azure CDN, Akamai CDN, etc.

Quality of Experience (QoE) is referred as level of user's satisfaction, enjoyment, learning and evaluation about the services or products. Recently quality of experience is used for improvement in product development life cycle after getting feedback from end users and service providers also use QoE to measure quality of services (QoS) of their services. Cloud computing provides services such as storage, web hosting, operating system environment, application development platform and CPU resources pay per use. End users are accessing cloud services via mobile apps, but enormous amount of temporary/cache data is generated by apps, so internal storage of mobile devices are filled quickly. Mobile device without any space in internal storage has huge impact on the performance when accessing the cloud services, which degrade the QoE of end users for particular cloud app and services. This paper presents the results of experiments conducted using two mobile devices HTC and Samsung to analyze the impact on end user's QoE during accessing cloud, when internal storage of HTC mobile device is filled and Samsung having 10 GB free space. Finally, on the basis of experimental results future changes in cloud apps are suggested for service provider to improve end user's QoE.

Online video transmissions over wireless networks are rising in popularity and have already become part of our daily life. In the meantime, it is necessary to address a number of challenges ranging from the scarce resources, time-varying, and high error rates, to the fluctuating bandwidth, unveiling the need for an adaptive mechanism to ensure a good video transmission. Adaptive Forward Error Correction (FEC) techniques with Quality of Experience (QoE) assurance are appropriate to deliver QoE-aware video data to wireless users in dynamic and high error rates networks. This paper proposes an adaptive Video-aware FEC and Fuzzy Logic-based mechanism to shield realtime video transmissions against packet loss in wireless networks, improving both user experience and the usage of resources. The benefits and drawbacks of the proposed mechanism compared with exiting work are demonstrated through simulations and assessed with QoE metrics.

We present a novel and complete tool-set for evaluating the delivery quality of MPEG video transmissions in simulations of a network environment. This tool-set is based on the EvalVid framework. We extend the connecting interfaces of EvalVid to replace its simple error simulation model by a more general network simulator like NS2. With this combination, researchers and practitioners in general can analyze through simulation the performance of real video streams, i.e. taking into account the video semantics, under a large range of network scenarios. To demonstrate the usefulness of our new tool-set, we point out that it enables the investigation of the relationship between two popular objective metrics for Quality of Service (QoS) assessment of video quality delivery: the PSNR (Peak Signal to Noise Ratio) and the fraction of decodable frames. The results show that the fraction of decodable frames reflects well the behavior of the PSNR metric, while being less time-consuming. Therefore, the fraction of decodable frames can be an alternative metric to objectively assess through simulations the delivery quality of transmission in a network of publicly available video trace files.