Efficiency Of Video Transmission Research Articles

Cross-Layer Wireless Resource Allocation Method Based on Environment-Awareness in High-Speed Mobile Networks

In high-speed mobile scenarios characteristic of the Fifth Generation Mobile Networks (5G) environment, the user video experience may be compromised due to concurrent access by numerous users and frequent base station transitions. Addressing this issue, this study introduces a cross-layer resource allocation model that integrates environmental awareness and is tailored for the exigencies of high-speed mobile networks. The paper delves into the challenges engendered by rapid mobility and extensive user access within the 5G environment and critiques the constraints of prevalent resource allocation methodologies. The model delineated herein is conceptualized as an optimization challenge and characterized as a nonlinear, NP-hard problem. In response to this challenge, this study advocates a novel streaming media transmission algorithm underpinned by edge computing, in tandem with an environment-aware wireless resource allocation algorithm. The article articulates the foundational principles and operational modalities of these algorithms, underscoring the significance of environmental cognizance in resource distribution and the efficacy of edge computing in increasing video transmission efficiency. Empirical validation, achieved through simulation experiments, corroborates the efficacy of the proposed approach. Comparative analysis reveals that, relative to conventional methodologies, the proposed framework significantly improves video transmission quality and user experience, particularly in contexts characterized by frequent network fluctuations and high user densities. This research contributes novel insights and pragmatic solutions to optimize video transmission in existing 5G and prospective network paradigms.

OJUMP: Optimization for joint unicast‐multicast panoramic VR live streaming system

AbstractVirtual reality (VR) videos have garnered increasing attention due to their ability to deliver immersive multimedia experiences to users. Nonetheless, the demanding requirements of high bandwidth and low latency for VR services present significant challenges, especially in the context of network infrastructure. In this article, we introduce a novel system called OJUMP, aimed at Optimizing the transmission and distribution of Joint Unicast‐Multicast Panoramic video field of view live streaming using Bit Index Explicit Replication (BIER). The proposed system encompasses a BIER‐based network transmission approach that enhances video transmission efficiency and facilitates large‐scale service deployment. Furthermore, OJUMP includes a dynamic disk‐state‐based tile slice distribution storage algorithm and a prefetch method designed for edge nodes to enhance the distribution efficiency of live streaming services. Experimental results illustrate that the proposed system offers distinct advantages over the baseline system in terms of bandwidth, latency, disk utilization, and central processing unit utilization.

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.

A Scalable Video Multicast Scheme Based on User Demand Perception and D2D Communication.

With the widespread application of 5G technology, there has been a significant surge in wireless video service demand and video traffic due to the proliferation of smart terminal devices and multimedia applications. However, the complexity of terminal devices, heterogeneous transmission channels, and the rapid growth of video traffic present new challenges for wireless network-based video applications. Although scalable video coding technology effectively improves video transmission efficiency in complex networks, traditional cellular base stations may struggle to handle video transmissions for all users simultaneously, particularly in large-scale networks. To tackle this issue, we propose a scalable video multicast scheme based on user demand perception and Device-to-Device (D2D) communication, aiming to enhance the D2D multicast network transmission performance of scalable videos in cellular D2D hybrid networks. Firstly, we analyze user interests by considering their video viewing history and factors like video popularity to determine their willingness for video pushing, thereby increasing the number of users receiving multicast clusters. Secondly, we design a cluster head selection algorithm that considers users' channel quality, social parameters, and video quality requirements. Performance results demonstrate that the proposed scheme effectively attracts potential request users to join multicast clusters, increases the number of users in the clusters, and meets diverse user demands for video quality.

Floop: An efficient video coding flow for unmanned aerial vehicles

AbstractUnder limited transmission conditions, many factors affect the efficiency of video transmission. During the flight of an unmanned aerial vehicle (UAV), frequent network switching often occurs, and the channel transmission condition changes rapidly, resulting in low‐video transmission efficiency. This paper presents an efficient video coding flow for UAVs working in the 5G nonstandalone network and proposes two bit controllers, including time and spatial bit controllers, in the flow. When the environment fluctuates significantly, the time bit controller adjusts the depth of the recursive codec to reduce the error propagation caused by excessive network inference. The spatial bit controller combines the spatial bit mask with the channel quality multiplier to adjust the bit allocation in space to allocate resources better and improve the efficiency of information carrying. In the spatial bit controller, a flexible mini graph is proposed to compute the channel quality multiplier. In this study, two bit controllers with end‐to‐end codec were combined, thereby constructing an efficient video coding flow. Many experiments have been performed in various environments. Concerning the multi‐scale structural similarity index and peak signal‐to‐noise ratio, the performance of the coding flow is close to that of H.265 in the low bits per pixel area. With an increase in bits per pixel, the saturation bottleneck of the coding flow is at the same level as that of H.264.

Audio-Visual Autoencoding for Privacy-Preserving Video Streaming

The demand of sharing video streaming extremely increases due to the proliferation of Internet of Things (IoT) devices in recent years, and the explosive development of artificial intelligent (AI) detection techniques has made visual privacy protection more urgent and difficult than ever before. Although a number of approaches have been proposed, their essential drawbacks limit the effect of visual privacy protection in real applications. In this article, we propose a cycle vector-quantized variational autoencoder (cycle-VQ-VAE) framework to encode and decode the video with its extracted audio, which takes the advantage of multiple heterogeneous data sources in the video itself to protect individuals’ privacy. In our cycle-VQ-VAE framework, a fusion mechanism is designed to integrate the video and its extracted audio. Particularly, the extracted audio works as the random noise with a nonpatterned distribution, which outperforms the noise that follows a patterned distribution for hiding visual information in the video. Under this framework, we design two models, including the frame-to-frame (F2F) model and video-to-video (V2V) model, to obtain privacy-preserving video streaming. In F2F, the video is processed as a sequence of frames; while, in V2V, the relations between frames are utilized to deal with the video, greatly improving the performance of privacy protection, video compression, and video reconstruction. Moreover, the video streaming is compressed in our encoding process, which can resist side-channel inference attack during video transmission and reduce video transmission time. Through the real-data experiments, we validate the superiority of our models (F2F and V2V) over the existing methods in visual privacy protection, visual quality preservation, and video transmission efficiency. The codes of our model implementation and more experimental results are now available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ahahnut/cycle-VQ-VAE</uri> .

A Novel Video Transmission Optimization Mechanism Based on Reinforcement Learning and Edge Computing

As we know, the video transmission traffic already constitutes 60% of Internet downlink traffic. The optimization of video transmission efficiency has become an important challenge in the network. This paper designs a video transmission optimization strategy that takes reinforcement learning and edge computing (TORE) to improve the video transmission efficiency and quality of experience. Specifically, first, we design the popularity prediction model for video requests based on the RL (reinforcement learning) and introduce the adaptive video encoding method for optimizing the efficiency of computing resource distribution. Second, we design a video caching strategy, which adopts EC (edge computing) to reduce the redundant video transmission. Last, simulations are conducted, and the experimental results fully demonstrate the improvement of video quality and response time.

Exploiting user preferences to reduce bandwidth requirements for VoD services with client caching

Internet traffic is already dominated by video streaming applications. The quality of transmitted videos and the number of content available online is expected to increase in the upcoming years, which will further pressure the available network infrastructures. In this scenario, Video-on-Demand services require that distribution mechanisms improve the efficiency of video transmission, which impacts the network performance and system scalability. The efficiency of distribution is strongly related to the operating costs of the providers of such services. The most efficient methods available for Video-on-Demand distribution use strategies that combine multicast transmission and the storage capacity of the user's equipment. This paper presents a new method for Video-on-Demand distribution that explores the client storage capabilities by modelling the users' preferences with a Hidden Markov Model. The efficiency of the proposed method is demonstrated through computational simulations of different scenarios, including a sample of real users' activity. Our results indicate that the proposed scheme substantially outperforms the current Video-on-Demand distribution mechanisms in terms of network bandwidth consumption, significantly reducing operation costs by improving the system scalability.

A caching‐enabled light control scheme for aerobics video

The video transmission traffic has account for 60% Internet traffic, and how to optimize the video transmission efficiency has become a major challenge. Especially, the aerobics video is strongly subjected to both temporal locality and spatial locality, and the related contents are also dynamically changeable with the precise and complex movements. Thus, in the age of multimedia, the transmission optimization of aerobics video has the considerable research value. In this paper, a transmission control scheme for aerobics video is proposed. This scheme has the light feature, which is embodied by one heuristic video compression method. Then, this paper realizes the effective congestion control based on the adaptive rate design. Meanwhile, video compression and congestion control are performed in the simultaneous way, and they are completed by using the explored in‐network cache. Given this, the simple cache allocation method is presented at last. Two baselines are compared by evaluating packet loss rate, transmission time and delay jitter, and the experiment results demonstrate the efficiency of the proposed transmission scheme.

Low-Cost Approach for Improving Video Transmission Efficiency in WVSN

The wireless visual sensor network (WVSN) as a new emerged intelligent visual system, has been applied in many video monitoring sites. However, there is still great challenge because of the limited wireless network bandwidth. To resolve the problem, we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image frames. Firstly, an adaptively learning background model is improved to discover successive similar image frames from the inputting video sequence. Then, the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element dynamics. Furthermore, a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or not. To evaluate the trade-off performance of the proposed method, it is tested on the CDW-2014 dataset, which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes, and the misdetection rate of the undetected foreground frames is near to zero. Compared to the original stream, it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.

FemtoCaching: Wireless Content Delivery Through Distributed Caching Helpers

Video on-demand streaming from Internet-based servers is becoming one of the most important services offered by wireless networks today. In order to improve the area spectral efficiency of video transmission in cellular systems, small cells heterogeneous architectures (e.g., femtocells, WiFi off-loading) are being proposed, such that video traffic to nomadic users can be handled by short-range links to the nearest small cell access points (referred to as “helpers”). As the helper deployment density increases, the backhaul capacity becomes the system bottleneck. In order to alleviate such bottleneck we propose a system where helpers with low-rate backhaul but high storage capacity cache popular video files. Files not available from helpers are transmitted by the cellular base station. We analyze the optimum way of assigning files to the helpers, in order to minimize the expected downloading time for files. We distinguish between the uncoded case (where only complete files are stored) and the coded case, where segments of Fountain-encoded versions of the video files are stored at helpers. We show that the uncoded optimum file assignment is NP-hard, and develop a greedy strategy that is provably within a factor 2 of the optimum. Further, for a special case we provide an efficient algorithm achieving a provably better approximation ratio of 1-(1-1/d )d, where d is the maximum number of helpers a user can be connected to. We also show that the coded optimum cache assignment problem is convex that can be further reduced to a linear program. We present numerical results comparing the proposed schemes.