High-quality Video Transmission Research Articles

Energy Efficient and Reliable High Quality Video Transmission Architecture for Wireless Sensor Networks

Energy Efficient and Reliable High Quality Video Transmission Architecture for Wireless Sensor Networks

A Comparison of Machine Learning-Based and Conventional Technologies for Video Compression

The growing demand for high-quality video transmission over bandwidth-constrained networks and the increasing availability of video content have led to the need for efficient storage and distribution of large video files. To improve the latter, this article offers a comparison of six video compression methods without loss of quality. Particularly, H.255, VP9, AV1, convolutional neural network (CNN), recurrent neural network (RNN), and deep autoencoder (DAE). The proposed decision is to use a dataset of high-quality videos to implement and compare the performance of classical compression algorithms and algorithms based on machine learning. Evaluations of the compression efficiency and the quality of the received images were made on the basis of two metrics: PSNR and SSIM. This comparison revealed the strengths and weaknesses of each approach and provided insights into how machine learning algorithms can be optimized in future research. In general, it contributed to the development of more efficient and effective video compression algorithms that can be useful for a wide range of applications.

IMPLEMENTACIÓN DE UN SISTEMA DE COMUNICACIÓN Y VIDEOVIGILANCIA INTEGRADO EN UN UAV PARA MISIONES ESTRATEGICAS

Unmanned Aerial Vehicles (UAVs) have been widely used in the military for monitoring and national defence activities. One of the main advantages of using UAVs is that they reduce loss of life, as they can be controlled remotely or with the help of algorithms, allowing high-risk manoeuvres to be carried out without fatal consequences. One of the objectives of using UAV systems is to reduce the costs of acquiring and operating conventional aircraft. One of the main problems in the development of UAVs the design and dimensioning of communication systems. This paper presents the development of a communication system for strategic missions with the validation of the communication link calculated through the implementation of a video surveillance system integrated in a UAV. The equipment of the communication and video transmission system was based on free commercial hardware and free software that allows the sending of high-resolution images. The UAV operation tests have allowed maintaining the telemetry link and high-quality video transmission at approximately 5 km. Key Words: energy efficiency, historic districts, 3D cities, building typology, categorization tool

Comparative Analysis of Telepresence Robots’ Video Performance: Evaluating Camera Capabilities for Remote Teaching and Learning

The COVID-19 outbreak demonstrated the viability of various remote working solutions, telepresence robots (TPRs) being one of them. High-quality video transmission is one of the cornerstones of using such solutions, as most of the information about the environment is acquired through vision. This study aims to compare the camera capabilities of four models of popular telepresence robots using compact reduced LogMAR and Snellen optometry charts as well as text displayed on a projector screen. The symbols from the images are extracted using the Google Vision OCR (Optical Character Recognition) software, and the results of the recognition are compared with the symbols on the charts. Double 3 TPR provides the best quality images of optometric charts, but the OCR results of measurements of the image on the projector do not show the clear advantage of one single model over the others. The results demonstrated by Temi 2 and Double 3 TPRs are generally better than the others, suggesting that these TPRs are more feasible to be used in teaching and learning scenarios.

Performance Evaluation of AVC and HEVC for E-Learning: Optimizing Quality and Reducing Bandwidth Usage

E-learning has experienced a surge in popularity, particularly during and after the COVID-19 pandemic. Online learning has proven to be a vital tool for students and educators to continue academic activities while adhering to social distancing guidelines and during the times of natural disasters that disrupt the conventional learning environments. It also offers accessibility to disabled students and those facing challenges to reach to the traditional learning. But due to increased demand, it is crucial to optimize cost of transmission while minimizing bandwidth usage while maintaining high-quality video transmission. To optimize cost and reduce network load, it is essential to minimize bandwidth usage while maintaining high-quality video. In response to this need, we present a novel dataset consisting of four e-learning scenarios. We encoded this dataset using various resolutions, bit rates, and encoder presets, and evaluated it in terms of encoding time, and quality using full-reference objective quality metrics such as MSE, PSNR, and SSIM. After experimenting with more than 1400 videos and configurations of encoders and codecs, we found out that it is possible to transmit videos in exceptional quality at bitrates as low as 5 Mbps for e-learning scenarios. We also present detailed results about correlation between file size, quality and encoding time to make optimizations for specific bandwidth, target quality or encoding speed.

ПІДВИЩЕННЯ ЯКОСТІ СПРИЙНЯТТЯ ВІДЕО В БЕЗПРОВІДНИХ ПРОГРАМНО-КОНФІГУРОВАНИХ МЕРЕЖАХ З ВИКОРИСТАННЯМ МУЛЬТИПІДКЛЮЧЕННЯ

This article presents a method to enhance the quality of video streaming using multi-connection in software-defined wireless networks. The method utilizes bonding technology to combine various physical network adapters into a single integrated device, effectively utilizing network resources and ensuring high-quality video transmission. The study also includes an analysis of the use of the Peak Signal-to-Noise Ratio (PSNR) as a quality perception metric and the method's response to deteriorating communication channel parameters. The results indicate that an increase in packet loss leads to a decrease in PSNR values. This is explained by the fact that packet loss introduces errors or noise into the compressed signal, reducing its accuracy compared to the original. Specifically, packet loss can be caused by insufficient network bandwidth when the volume of data attempting to pass through the network exceeds its capacity. In such cases, packet loss can lead to a deterioration in the quality of the video stream. On the other hand, packet loss due to radio signal instability may result from external factors such as obstacles or interference, which can lead to packet loss even with sufficient network bandwidth. These losses can also impact the quality of the video stream and reduce PSNR values. During the study, it was observed that an equal percentage of packet losses caused by insufficient network bandwidth and losses due to radio signal instability have different effects on image perception quality. In general, the overall research results confirm the potential for significant improvement in the quality of service for wireless network users using the proposed method, without requiring additional network resource expenditures. This can have significant practical implications for advancing network technologies and ensuring high-quality video data transmission in modern wireless networks.

An enhanced algorithm for improving real-time video transmission for tele-training education.

Tele-training in surgical education has not been effectively implemented. There is a stringent need for a high transmission rate, reliability, throughput, and reduced distortion for high-quality video transmission in the real-time network. This work aims to propose a system that improves video quality during real-time surgical tele-training. The proposed approach aims to minimise the video frame’s total distortion, ensuring better flow rate allocation and enhancing the video frames’ reliability. The proposed system consists of a proposed algorithm for Enhancing Video Quality, Distorting Minimization, Bandwidth efficiency, and Reliability Maximization called (EVQDMBRM) algorithm. The proposed algorithm reduces the video frame’s total distortion. In addition, it enhances the video quality in a real-time network by dynamically allocating the flow rate at the video source and maximizing the transmission reliability of the video frames. The result shows that the proposed EVQDMBRM algorithm improves the video quality with the minimized total distortion. Therefore, it improves the Peak Signal to Noise Ratio (PSNR) average by 51.13 dB against 47.28 dB in the existing systems. Furthermore, it reduces the video frames processing time average by 58.2 milliseconds (ms) against 76.1, and the end-to-end delay average by 114.57 ms against 133.58 ms comparing to the traditional methods. The proposed system concentrates on minimizing video distortion and improving the surgical video transmission quality by using an EVQDMBRM algorithm. It provides the mechanism to allocate the video rate at the source dynamically. Besides that, it minimizes the packet loss ratio and probing status, which estimates the available bandwidth.

협대역 무선 전술 네트워크를 위한 고품질 동영상 전송 시스템의 설계, 구현 및 검증

본 논문은 협대역(수십kHz) 무선 전술 네트워크에서 동영상 스트리밍을 수행하는 영상 압축 및 전송 시스템의 설계, 구현 및 검증 결과에 대해 기술한다. 제안 시스템은 실시간 동영상 전송을 위한 충분한 주파수 대역폭 확보가 어려운 협대역 무선 전술 네트워크 환경에서 고품질의 동영상 서비스 제공을 목표로 설계되었으며, 이를 달성할 수 있도록 응용계층 부호화, 선택적 재전송 등의 영상전송 네트워크 기술, 소프트웨어기반 high efficiency video codec (HEVC) 코덱 및 영상 전처리 등의 영상 압축 기술 등을 통합하여 구현되었다. 구현된 시스템은 실내 및 실외 환경에서 검증되었으며 주어진 전송속도에 따라 설정된 영상 프로파일에 의해 영상품질 요구 성능을 만족하는 것을 확인하였다.

Performance evaluation of the effect of traffic decentralization with mobile edge computing

In the centralized architecture of traditional mobile cellular network, every traffic of mobile terminals has to traverse the centered network core and then deliver to the corresponding service mobile terminals. Since the traffic load is concentrated on the central network core, it is difficult to deliver the explosive amount of network traffic and guarantee latency requirements of diversifying applications. In order to solve this problem, the mobile edge computing (MEC) architecture that distributes the traffic load by locating the computing and caching server closer to the terminal is currently being developed. In this paper, the effect of distribution of mobile traffic with MEC is evaluated in terms of throughput enhancements and latency reduction. The simulations were conducted using Mininet to determine what performance gains can be obtained when applying the MEC architecture to the mobile cellular network. As a result, under the same link bandwidth condition, MEC architecture provides an increase in data rates of approximately 265% for high-quality video transmission and about 162% for low-quality video transmission, and shows the reduced delay of packet arrival times.

Real-time video frame differentiation in multihomed VANETs

Providing high quality video transmission in VANETs is very challenging due to the highly dynamic, unpredictable topology, and low bandwidth characteristics. In this article, we design a system able to optimally transmit RTP video streams in an IP-based multihomed VANET. By splitting the video through its different frame types in the array of multihomed paths from the center node of the network to the clients, the system can then send critical frames, depending on the used coding standard, through a more reliable path, in order to improve the video performance even if the quality is degraded due to bad signal reception. Two different content-based multihomed video distribution schemes have been proposed: linear selection, where the system is able to select more reliable paths for higher prioritized segments; and adaptive selection, where the paths are sensed to assess their congestion level in a real-time approach. Through real deployment of these approaches in a real vehicular scenario, with mobility, handovers and multihoming, the proposed approaches achieved a strong decrease in the loss percentage, with a maximum of approximately 60.4%, greatly improving the video quality while on the move.

Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use

This research provides a study on a smart body area network (SmartBAN) physical layer (PHY), as an of the Internet of medical things (IoMT) technology, for an advanced human monitoring system in industrial use. The SmartBAN provides a new PHY and a medium access control (MAC) layer, improving its performance and providing very low-latency emergency information transmission with low energy consumption compared with other wireless body area network (WBAN) standards. On the other hand, IoMT applications are expected to become more advanced with smarter wearable devices, such as augmented reality-based human monitoring and work support in a factory. Therefore, it is possible to develop more advanced human monitoring systems for industrial use by combining the SmartBAN with multimedia devices. However, the SmartBAN PHY is not designed to transmit multimedia information such as audio and video. To address this issue, multilevel phase shift keying (PSK) modulation is applied to the SmartBAN PHY, and the symbol rate is improved by setting the roll-off rate appropriately to realize the system. The numerical results show that a sufficient link budget, receiver sensitivity and fade margin were obtained even when those approaches were applied to the SmartBAN PHY. The results indicate that these techniques are required for high-quality audio or video transmission, as well as vital sign data transmission, in a SmartBAN.

Video coding and constant quality evaluation using 4k aomenc-AV1 and rav1e-AV1 formats

It is much expected from the relatively novel, open, royalty-free AV1 (Alliance for Open Media (AOMedia) Video 1) standard. At this moment, there are many new variants of AV1 format. It is designed for efficient video internet delivery and high-quality video transmission. AV1 is recognized as Google?s VP9 format successor. One of the reference tools used so far for testing AV1 is libaom- AV1. Nevertheless, due to its time-consuming performance, there are now different available standalone solutions for experimental analysis. Here, one such solution AOMedia?s standalone aomenc (aomenc-AV1) is tested in order to analyze quality assessment based on constant quality constraint factor. Three different metrics are calculated for various 4k video content of the same frame rate. Moreover, rav1e implementation was tested for the same visual data, where rav1e-AV1 also represents an AV1 video encoder, which is considered reliable and suitable in most cases, where libaom is not applicable. In this paper, the comparison results between aomenc-AV1 and rav1e-AV1 are shown.

A novel solution for real time video and path quality, and latency minimization: tele-training in surgical education

Surgical Tele-training has not been effectively implemented in telemedicine because of strict requirements for throughput and delay minimization during real-time high-quality video transmission. Therefore, the aim of this paper is to propose a new solution for surgical Tele-training with improved quality of real-time surgical video’s transmission, through put and minimized end-to-end delay required during the training session. The proposed system consists of an Enhanced Path Quality, Video Quality, and Latency Minimization (EPQVQaLM) algorithm to reduce network latency and improve the quality of real-time video transmission by including predicted data delivery time and packet drop probability in path quality calculation. The system also consists of cloud-based online video transcoding for economic transcoding of large-volume videos while guarantying the QoS parameter in case of additional resource requirement. The result shows that EPQVQaLM achieves improvements in PSNR by an average of 5db over the state of art solution. Furthermore, the proposed solution can reduce the processing time and end-to-end delay by an average of 32.6 ms and 33.93 ms over the state of art solution respectively. The proposed system concentrates on reducing end-to-end transmission delay and improves quality of surgical video transmission by using an EPQVQaLM technique. Meanwhile, it provides the mechanism for dropping the packets and re-routing packets to the different path before congestion becomes critical. Thus, this study provides an efficient mechanism to intelligently distribute data chunks over multiple paths.

Diagonal XOR-Based FEC Method to Improve Burst-Loss Tolerance for 4K/8K UHDTV Transmission

Broadcasters are converting their live program production facilities to Internet Protocol (IP)-networks. The introduction of IP-networks has many possibilities such as cost reduction and remote production, and various efforts are underway. At present, the main issue is to consider the construction of equipment for 2K high definition television (HDTV), and it is expected that the introduction of higher speed 4K and 8K ultra-HDTV (UHDTV) will accelerate in the future. In video transmissions using high-speed signals, video degradation due to packet loss such as random loss and burst loss that occurs in the network-switch of a transmission path becomes a problem. In the case of 4K and 8K UHDTV transmissions, since the number of packets per unit time increases, this problem becomes more serious. Therefore, it is indispensable to apply quality compensation technology to achieve high-quality video transmission. Forward error correction (FEC) using erasure codes is a key compensation method for recovering lost packets at the receiving side in unidirectional transmission. In this paper, we propose an exclusive OR-based FEC method with improved burst loss tolerance. Using this method, we have developed a transmission device for uncompressed 8K UHDTV signals over IP-networks using 100-Gb/s Ethernet packets. Experimental and simulation results clarified that the proposed method can improve the number of recoverable packets at the time of burst loss to twice as many as the conventional Pro-MPEG FEC method.

Scalable Virtual Network Video-Optimizer for Adaptive Real-Time Video Transmission in 5G Networks

The increasing popularity of video applications and ever-growing high-quality video transmissions (e.g., 4K resolutions), has encouraged other sectors to explore the growth of opportunities. In the case of health sector, mobile Health services are becoming increasingly relevant in real-time emergency video communication scenarios where a remote medical experts’ support is paramount to a successful and early disease diagnosis. To minimize the negative effects that could affect critical services in a heavily loaded network, it is essential for 5G video providers to deploy highly scalable and priorizable in-network video optimization schemes to meet the expectations of a large quantity of video treatments. This paper presents a novel 5G Video Optimizer Virtual Network Function (vOptimizerVNF) that leverages the latest technologies in 5G and video processing to address this important challenge. Advanced traffic filtering is coupled with Scalable H.265 video coding to enable run-time bandwidth-saving video optimization without compromising Quality of Service (QoS); kernel-space video processing is introduced to achieve further performance gains; and the use of a Virtual Network Function (VNF) facilitates dynamic deployment of virtualized video optimizers to achieve scalability and flexibility in this service. The proposed approach is implemented in a realistic 5G testbed and empirical results demonstrate the superior scalability and performance achieved.

Energy-Efficient and Robust Tensor-Encoder for Wireless Camera Networks in Internet of Things

With the development of Internet of Things (IoT), wireless camera networks have been widely deployed owing to its low-cost of deployment and maintenance as well as flexibility. However, it is challenging for wireless camera networks to provide energy-efficient and robust video transmissions, since the multihop wireless communications have limited bandwidth and low link quality. Besides, the camera nodes are usually battery-powered. State-of-the-art coding schemes adopt complex predictive encoding methods, thus leading to high complexity and power consumption. In this paper, we propose a novel tensor-encoder (i.e., being a randomly generated mask) for energy-efficient and robust video transmissions over unreliable wireless networks. First, we adopt a novel algebraic framework, i.e., the low-tubal-rank tensor model, to capture the strong spatiotemporal correlations within video data. Secondly, we design a mask-encoder, modeled as a mask-sampling process, that dramatically reduces the transmission burden. Then, we propose an alternating minimization algorithm as the corresponding mask-decoder. Thirdly, we prove that the proposed decoder guarantees exponential convergence to the global optima. For an $n\times n\times t$ video stream with tubal-rank $r\ll n$ , the required sampling complexity is $O(nr^2t\;\log^3\;n)\ll n^2t$ and the computational complexity is $O(n^2rt\;\log^2\; n)$ . Finally, based on synthetic data, real-world data and our wireless camera network testbed, we compare the proposed scheme with existing methods and obtain high quality video transmissions at a compression ratio of 20 percent.

Scalable Video Coding Using Accordion Discrete Wavelet Transform and Tucker Decomposition for Multimedia Applications

The digital world demands the transmission and storage of high quality video for streaming and broadcasting applications, the constraints are the network bandwidth and the memory of devices for the various multimedia and scientific applications, the video consists of spatial and temporal redundancies. The objective of any video compression algorithm is to eliminate the redundant information from the video signal during compression for effective transmission and storage. The correlation between the successive frames has not been exploited enough by the current compression algorithms. In this paper, a novel method for video compression is presented. The proposed model, applies the transformation on set of group of pictures (GOP). The high spatial correlation is achieved from the spatial and temporal redundancy of GOP by accordion representation and this helps to bypass the computationally demanding motion compensation step. The core idea of the proposed technique is to apply Tucker Decomposition (TD) on the Discrete Wavelet Transform (DWT) coefficients of the Accordion model of the GOP. We use DWT to separate the video in to different sub-images and TD to efficiently compact the energy of sub-images. The blocking artifacts will be considerably eliminated as the block size is huge. The proposed method attempts to reduce the spatial and temporal redundancies of the video signal to improve the compression ratio, computation time, and PSNR. The experimental results prove that the proposed method is efficient especially in high bit rate and with slow motion videos.

MCast: High-Quality Linear Video Transmission with Time and Frequency Diversities.

Uncoded linear video transmission has recently attracted people's attention due to its capacity to provide robust and scalable transmission. However, in reality, with the fluctuation of the wireless channels, the received quality may not be good enough. In such a case, the data may need to be transmitted multiple times to exploit both the time and frequency diversities to improve the received quality. Such a problem has never been investigated in the literature of uncoded video transmission. To resolve the problem, in this paper, we propose a framework, named MCast, to utilize the time and frequency diversities to achieve high-quality linear video transmission. We study how to optimally allocate the power and assign the channels at each time slot to the source data such that the overall performance is maximized. Specifically, we first derive a closed-form optimal power allocation solution for any given channel assignment. With the optimal power allocation, we then propose a suboptimal channel assignment scheme, where we sort the channels with their gains and assign the channels one-by-one to the corresponding block that can reduce the most reconstruction error. Finally, we compare MCast system with four other systems that are based on Softcast and Parcast, and simulation results show that MCast system can achieve better performance in terms of both PSNR performance and visual quality.

Improving Multipath Video Transmission With Raptor Codes in Heterogeneous Wireless Networks

Supported by the latest technical innovations mobile users are able to simultaneously receive real-time streaming services with different radio access technologies (e.g. LTE and Wi-Fi). The stream control transmission protocol (SCTP) is a vitally important transport protocol to enable concurrent multipath transfer (CMT) in heterogeneous wireless networks with multihomed terminals. However enabling CMT of real-time video streaming to multihomed mobiles is challenged with key technical dilemmas: 1) high-quality real-time video transmission is constrained by stringent requirements in delay and throughput; 2) wireless networks are bandwidth-limited and error-prone; and 3) the congestion control and packet retransmission modules in the SCTP may incur frequent deadline violations and throughput fluctuations. Motivated by addressing these challenging problems this research proposes a Video and Raptor code aware CMT (CMT-VR) solution. First we develop a mathematical model to formulate the utility maximization problem of multipath real-time video delivery over parallel wireless networks. Second we present a transmission framework that includes online packet scheduling Raptor coding adaptation and retransmission control algorithms. CMT-VR is distinct from the existing SCTPs in leveraging the video frame priority and rateless Raptor coding. The performance verification is conducted by means of system evaluations over real wireless networks and extensive semiphysical emulations in the Exata platform. Evaluation results demonstrate that CMT-VR achieves appreciable improvements over the reference schemes in perceived video quality goodput and end-to-end delay.

Protection Against Defecting Attack and Enhanced Channel Allocation for Video Streaming in CRN

Arising issue of spectrum scarcity is resolved by emerging cognitive radio technology. In which, unoccupied portion of licensed spectrum would be granted for unlicensed user in order to upgrade the spectrum utilization. Spectrum sensing is an enabling task for labeling the spectrum holes and away from spectrum congestion in cognitive radio networks. Impact of secondary user’s cooperation developed cooperative spectrum sensing for effective spectrum sensing, many secondary users are to be associated in sensing the idle channels. Adversary secondary users (SU) mimic as primary user in cooperative sensing scheme, called primary user emulation attack (PUEA). If any SU forges the sensing data, that is known as spectrum sensing data falsification attack (SSDF). Rescuing the network against these two attacks is the major issue of cognitive radio networks. To address these challenges, we propose cognitive radio network to ensure security in cooperative spectrum sensing for video streaming application. Sensing based clustering algorithm assembles the cognitive radios into clusters to mitigate cooperative overhead and consumed energy. In order to avert PUEA attacks, we introduce authentication signature ID algorithm which provides authority for licensed user to use spectrum. To diminish SSDF attacks, we exploit Hamming algorithm to abolish forgery node from spectrum sensing decision. Furthermore PRN channel allocation algorithm is employed to precisely deliver the video with high quality and lower delay. For video transmission in cognitive radio network, we need to compress the video frames by block truncation coding-pattern fitting in order to reduce bandwidth consumption. This compression technique offers good video transmission with high compression ratio in cognitive radio network, due to trade-off among quality, bit rate and decoding time. Our proposed framework demonstrates the simulation with improved detection rate of idle channel and reduced the delay for high quality video transmission.