Video Transmission In Networks Research Articles

A Survey on Video Streaming for Next-Generation Vehicular Networks

As assisted driving technology advances and vehicle entertainment systems rapidly develop, future vehicles will become mobile cinemas, where passengers can use various multimedia applications in the car. In recent years, the progress in multimedia technology has given rise to immersive video experiences. In addition to conventional 2D videos, 360° videos are gaining popularity, and volumetric videos, which can offer users a better immersive experience, have been discussed. However, these applications place high demands on network capabilities, leading to a dependence on next-generation wireless communication technology to address network bottlenecks. Therefore, this study provides an exhaustive overview of the latest advancements in video streaming over vehicular networks. First, we introduce related work and background knowledge, and provide an overview of recent developments in vehicular networking and video types. Next, we detail various video processing technologies, including the latest released standards. Detailed explanations are provided for network strategies and wireless communication technologies that can optimize video transmission in vehicular networks, paying special attention to the relevant literature regarding the current development of 6G technology that is applied to vehicle communication. Finally, we proposed future research directions and challenges. Building upon the technologies introduced in this paper and considering diverse applications, we suggest a suitable vehicular network architecture for next-generation video transmission.

Improving The Efficiency Of Video Transmission In Computer Networks

In-depth examination of current techniques for enhancing the efficiency of video transmission over digital networks is provided in this study. Due to the growing need for high-quality video content, optimizing video transmission is an important area of research. This review categorizes and in-depth examines a range of methods proposed in the literature to enhance video transmission effectiveness. ABR, DNN architecture, adaptive streaming, Quality of Service (QoS), error resilience, congestion control, video compression, and hardware acceleration for video provisioning are just a few of the cutting-edge techniques that are covered in the discussion, which ranges from the more traditional to the cutting-edge. This essay provides a methodical evaluation of the numerous tactics that are available, along with an analysis of their guiding principles, advantages, and disadvantages. The paper also offers a comparative analysis of various approaches, highlighting trends, gaps, and potential future research directions in this crucial domain, all of which help to create more efficient video compression and transmission paradigms in computer networks.

D2D-assisted user-centric adaptive video transmission in next generation cellular networks

Next-generation cellular networks need to provide seamless connectivity with higher data rates, increased capacity, and enhanced network coverage. As multimedia service demands in various heterogeneous devices grow rapidly compared to the underlying network’s capacity and bandwidth, the adaptation in multimedia streaming services is essential for providing satisfactory Quality of Experience (QoE). This paper develops a Device-to-Device (D2D)-assisted Utility-based Adaptive Multimedia (video) Streaming scheme (UAMS) using D2D communication in a 5th Generation (5G) cellular network where low-battery users may extend their streaming duration by spending lower reception energy with the help of D2D-assisted communication. The adaptation algorithm considers four utility functions: quality, power consumption, packet error ratio, and remaining battery of the user devices to adapt the bitrate dynamically and augment viewers’ experience. We formulate an optimization problem to maximize the joint utility function to provide the best adaptive multimedia content selected for transmission to the end-users either directly or via D2D Relay Nodes (DRNs) in every scheduling interval. We use a graph theoretic approach for choosing the best DRNs. Extensive simulations show the efficacy of the proposed scheme in terms of saved battery energy, churn rate, and QoE metrics compared to a few well-known existing schemes in the literature that do not use D2D communication.

Femtocell deployment for scalable video transmission in 5G networks

In this paper, we propose a femtocell deployment (FD) scheme to transmit scalable video traffic in 5G Heterogeneous Networks (HetNets). Our goal is to use the flexibility of scalable video coding (SVC) for providing video service for the maximum number of users with the best quality of experience (QoE) and the lowest power consumption. The number of required Femto Base Stations (FBSs) is determined by solving a special case of a multiple fractional knapsack problem (MFKP) with three objectives called MU, MQ, and MP. They are defined as maximizing the number of users receiving video service, maximizing the mean QoE, and minimizing power consumption, respectively. To this end, first, a near-optimal solution to the problem is achieved by genetic algorithm (GA). Then, we propose a maximum resource-efficient (ME) solution based on a greedy algorithm which is close to the near-optimal solution with lower computational complexity. The performance evaluation of the network with FD shows that deploying FBSs provides video streaming services for a greater number of users with higher QoE and lower power consumption compared to the network without FD. Moreover, the combination of FD and SVC improves the network performance compared to the combination with non-scalable video bitstreams. Due to the flexibility of scalable video, by using a scalable video bitstream, instead of increasing the number of FBSs, enough capacity for serving more users is provided by decreasing the number of received video layers.

Indoor Art Display System under Computer Virtual Reality Visual Video Communication Transmission Signal

This paper proposes a network video transmission system for indoor art display systems and visual video monitoring. Due to the problems of high mean square error, poor signal capture performance, and long timing jitter in current optical transmission signals, this paper proposes a low-rate narrowband IoT long-distance communication synchronization capture algorithm and optimizes the algorithm. The algorithm improves the visual center processing algorithm with high computational complexity into a simple look-up table calculation. This can simplify the calculation process and improve the system’s operating speed. Finally, the paper implements a video communication indoor art display system with compatible multiple network interfaces based on high-speed multimedia DSP.

High-Efficiency Multihomed Multimedia Transmission in Wireless Sensors

For real-time video transmission in multimedia sensor networks, efficient and reliable communication is very important. However, under actual conditions, when the sensor transmits data to the sink node, the link between the sensor and the sink node usually becomes unstable due to bandwidth limitations and unstable transmission links. Unstable data transmission will affect the normal data transmission of sensor nodes, and even cause sensor nodes to fail to communicate with sink nodes. These problems make users who use multimedia sensor networks have a poor user experience. In order to solve these problems, a brand-new multi-homed multi-connection system was designed for wireless multimedia sensors, and a prototype system was implemented. Compared with the traditional single-homed connection mode, the system can maintain a more stable connection under the same bandwidth, memory and CPU resource constraints.

Femtocell Deployment for Scalable Video Transmission in 5g Networks

Femtocell Deployment for Scalable Video Transmission in 5g Networks

An Authenticated Trust Based Security Mechanism for Video Transmission in Wireless Mesh Networks

An Authenticated Trust Based Security Mechanism for Video Transmission in Wireless Mesh Networks

Long-term rate control for concurrent multipath real-time video transmission in heterogeneous wireless networks

Concurrent multipath transmission provides an effective solution for streaming high-quality mobile videos in heterogeneous wireless networks. Rate control is commonly adopted in multimedia communication systems to fully utilize the available network bandwidth. This paper proposes a novel rate control for concurrent multipath video transmission. The existing rate control algorithms mainly adapt bit rate in the short-term pattern, i.e., without considering the long-term video transmission quality. We propose a long-term rate control scheme that takes into account the status of both the transmission buffer and video frames. First, a mathematical model is developed to formulate the non-convex problem of long-term quality maximization. Second, we develop a dynamic programming solution for online encoding bit rate control based on buffer status. The performance evaluation is conducted in a real test bed over LTE and Wi-Fi networks. Experimental results demonstrate that the proposed long-term rate control scheme achieves appreciable improvements over the short-term rate control schemes in terms of video quality and delay performance.

Compressed Sensing Based Low-Power Multi-View Video Coding and Transmission in Wireless Multi-Path Multi-Hop Networks

Wireless Multimedia Sensor Network (WMSN) is increasingly being deployed for surveillance, monitoring and Internet-of-Things (IoT) sensing applications where a set of cameras capture and compress local images and then transmit the data to a remote controller. Such captured local images may also be compressed in a multi-view fashion to reduce the redundancy among overlapping views. In this paper, we present a novel paradigm for compressed-sensing-enabled multi-view coding and streaming in WMSN. We first propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and independent decoders. The proposed architecture leverages the properties of CS to overcome many limitations of traditional encoding techniques, specifically massive storage requirements and high computational complexity. Then, we present a modeling framework that exploits the aforementioned coding architecture. The proposed mathematical problem minimizes the power consumption by jointly determining the encoding rate and multi-path rate allocation subject to distortion and energy constraints. Extensive performance evaluation results show that the proposed framework is able to transmit multi-view streams with guaranteed video quality at lower power consumption.

Efficient and Privacy Preserving Video Transmission in 5G-Enabled IoT Surveillance Networks: Current Challenges and Future Directions

Vision sensors in Internet of Things (IoT)-connected smart cities play a vital role in the exponential growth of video data, thereby making its analysis and storage comparatively tough and challenging. Those sensors continuously generate data for 24 hours, which requires huge storage resources, dedicated networks for sharing with data centers, and most importantly, it makes browsing, retrieval, and event searching a difficult and time-consuming job. Video summarization (VS) is a promising direction toward a solution to these problems by analyzing the visual contents acquired from a vision sensor and prioritizing them based on events, saliency, person's appearance, and so on. However, the current VS literature still lacks focus on resource-constrained devices that can summarize data over the edge and upload it to data repositories efficiently for instant analysis. Therefore, in this article, we carry out a survey of functional VS methods to understand their pros and cons for resource-constrained devices, with the ambition to provide a compact tutorial to the community of researchers in the field. Further, we present a novel saliency-aware VS framework, incorporating 5G-enabled IoT devices, which keeps only important data, thereby saving storage resources and providing representative data for immediate exploration. Keeping privacy of data as a second priority, we intelligently encrypt the salient frames over resource-constrained devices before transmission over the 5G network. The reported experimental results show that our proposed framework has additional benefits of faster transmission (1.8~13.77 percent frames of a lengthy video are considered for transmission), reduced bandwidth, and real-time processing compared to state-of-the-art methods in the field.

A Simulation Tool for Evaluating Video Streaming Architectures in Vehicular Network Scenarios

An integrated simulation tool called Video Delivery Simulation Framework over Vehicular Networks (VDSF-VN) is presented. This framework is intended to allow users to conduct experiments related to video transmission in vehicular networks by means of simulation. Research on this topic requires the use of many independent tools, such as traffic and network simulators, intermediate frameworks, video encoders and decoders, converters, platform-dependent scripting languages, data visualisation packages and spreadsheets, and some other tasks are performed manually. The lack of tools necessary to carry out all these tasks in an integrated and efficient way formed the motivation for the development of the VDSF-VN framework. It is managed via two user-friendly applications, GatcomSUMO and GatcomVideo, which allow all the necessary tasks to be accomplished. The first is primarily used to build the network scenario and set up the traffic flows, whereas the second involves the delivery process of the whole video, encoding/decoding video, running simulations, and processing all the experimental results to automatically provide the requested figures, tables and reports. This multiplatform framework is intended to fill the existing gap in this field, and has been successfully used in several experimental tests of vehicular networks.

Comparing application layer protocols for video transmission in IoT low power lossy networks: an analytic comparison

Wide-spread use of low power lossy networks (LLNs) in internet of things (IoT) raises numerous challenges to video transmission over those networks. Deployment of proper application layer protocols can overcome some of those challenges and be influential on video transmission over LLNs. Although there are many good researches on application layer protocols in IoT, none of them provide a review for these protocols in video transmission scenarios. In this paper a comparison among some of these protocols including AMQP, CoAP, DDS, MQTT, MQTT-SN, Websocket, XMPP, HTTP 1.1/2.0, RTP and its control protocols such as RTCP is performed. The comparison shows that the nature of LLNs make video transmission an impossible task for some of these protocols and a proper protocol such as CoAP should be adapted to be used in these low power and lossy environments.

High Performance GPU Tensor Completion With Tubal-Sampling Pattern

Data completion is a problem of filling missing or unobserved elements of partially observed datasets. Data completion algorithms have received wide attention and achievements in diverse domains including data mining, signal processing, and computer vision. We observe a ubiquitous tubal-sampling pattern in big data and Internet of Things (IoT) applications, which is introduced by many reasons such as high data acquisition cost, downsampling for data compression, sensor node failures, and packet losses in low-power wireless transmissions. To meet the time and accuracy requirements of applications, data completion methods are expected to be accurate as well as fast. However, the existing methods for data completion with the tubal-sampling pattern are either accurate or fast, but not both. In this article, we propose high-performance graphics processing unit (GPU) tensor completion for data completion with the tubal-sampling pattern. First, by exploiting the convolution theorem, we split a tensor least-squares minimization problem into multiple least-squares sub-problems in the frequency domain. In this way, massive parallelisms are exposed for many-core GPU architectures while still preserving high recovery accuracy. Second, we propose computing slice-level and tube-level tasks in batches to improve GPU utilization. Third, we reduce the data transfer cost by eliminating the accesses to the CPU memory inside algorithm loop structures. The experimental results show that the proposed tensor completion is both fast and accurate. Using synthetic data of varying sizes, the proposed GPU tensor completion achieves maximum 248.18×, 7, 403.27×, and 33.27× speedups over the CPU MATLAB implementation, GPU element-sampling tensor completion in the cuTensor-tubal library, and GPU high-performance matrix completion, respectively. With a 50 percent sampling rate, the proposed GPU tensor completion achieves a recovery error of 1.40e-5, which is comparable with that of the GPU element-sampling tensor completion and three orders of magnitude better than that of the GPU high-performance matrix completion. To utilize multiple GPUs in servers, we design a multi-GPU scheme for tubal-sampling tensor completion. The multi-GPU tensor completion achieves maximum 1.89× speedup on two GPUs versus on a single GPU for medium or big tensors. We further evaluate the performance of the proposed GPU tensor completion in three real applications, namely, video transmission in wireless camera networks, RF fingerprint-based indoor localization, and seismic data completion, and it achieves maximum speedups of 448.68×, 24.63×, and 311.54×, respectively. We integrate this high-performance GPU tensor completion implementation into the cuTensor-tubal library to support various applications.

Video Camera Latency Analysis and Measurement

All modern video cameras exhibit some latency between the scene being captured and the output video signal. This camera latency is significant to the overall latency of a video network transmission chain. Some real-time applications based on video sharing require very low latency and selecting the right camera is then crucial. We observed how video cameras operate regarding their latency. We proposed three methods to measure camera latency: timecode view, waveform shift, and screen photodetector methods. All methods can achieve a subframe resolution and precision of approximately 1 ms although with varying levels of automation, convenience and suitability for particular cameras. We discuss arrangements that affect measurement precision. We applied the proposed methods to a sample camera, and we show measurements results for several other cameras. The measurement results showed that the fastest cameras provide latencies lower than 5 ms, which should be fast enough even for demanding real-time applications. However, most cameras still exhibit latencies at the range of 1-3 video frames.

Key Information Recognition Algorithm for Mobile Network Video Propagation based on Discrete GMM-HMM

In view of the problem that mobile network video transmission is prone to traffic congestion, the key information identification is carried out to improve the mobility of mobile network video transmission. A key information recognition method for mobile network video transmission is proposed based on Gaussian Mixture Model (GMM-HMM). The key information after feature separation is input into the neural network classifier to detect and process the key information of mobile network video transmission, and the key information identification of mobile network video transmission is completed. The simulation results show that the proposed method has strong anti-jamming ability, high probability of accurate recognition and good convergence of GMM-HMM Model, which improves the recognition efficiency.

Suitability of Transport Techniques for Video Transmission in IP Networks

The paper discusses the problem of video transmission in an IP network. The authors consider the ability of using the most popular video codecs that use both the MPEG2 Transport Stream and Dynamic Adaptive Streaming over Hypertext Transfer Protocol (DASH). The main emphasis was given to ensuring the quality of service and quality assessment methods, taking into account not only the service- or network provider’s point of view but also the end user’s perspective. Two quality assessment approaches were presented, i.e. objective and subjective methods. The authors presented the results of the quality evaluation for H.264/MPEG-4, H.265/HEVC and VP9 codecs. The objective measurements, proved by statistical analysis of user opinion scores, confirmed the ability of using H.265 and VP9 codecs in both real time and streaming transmissions, while the quality of video streaming over HTTP with the H.264 codec proved inadequate. The authors also presented a connection between the dynamics of network bandwidth changing and MPEG-DASH mechanism operation and their influence on the quality experienced by users.

A QoE-Oriented Network Selection Mechanism in Heterogeneous Wireless Networks

In order to guarantee the UAV video uplink in heterogeneous networks, a novel access network selection mechanism was proposed in this paper. Due to the unknown Channel State Information(CSI), the issue was first modeled as the Multi-Armed Bandit (MAB) problem. Then, the reward function was built and the Quality of Experience (QoE) of video was considered. By means of three typical solving algorithms, decisions could be made between utilization and exploration. The simulation results show that the mechanism can effectively solve the network selection problem of video transmission in heterogeneous networks.

Quality of experience and quality of service‐aware handover for video transmission in heterogeneous networks

SummaryHeterogeneous networks offer a wide range of multimedia services, such as entertainment, advertising, and video conferences. In this multimedia scenario, users can access video content via heterogeneous wireless networks, such as LTE macro and small cells. Users also expect to receive real‐time videos with Quality of Experience (QoE) support, which is a challenging task due to the great diversity of radio base stations in such heterogeneous environments. In this article, we introduce a Quality of Service (QoS‐)/QoE‐ and Radio‐aware (SER) handover management algorithm for heterogeneous networks to provide video dissemination with QoS/QoE support. SER algorithm considers the analytic hierarchy process (AHP) to adjust the degree of importance of each criterion in order to select the appropriate radio base station that the mobile node must connect, allowing efficient handover decision making for video transmission with high user experience. Simulation results show that the SER algorithm delivered videos with significant improvement on QoE than existing handover algorithms.

An effective system for video transmission and error recovery mechanisms in multimedia networks

An effective system for video transmission and error recovery mechanisms in multimedia networks