Video Streaming Applications Research Articles

User Datagram Protocol Parametric Analysis for Maximum Transmission Unit in High-Definition Video Transmission for 1080p and 720p

This article analyses the parametric used for Maximum Transmission Unit (MTU) in User Datagram Protocol (UDP) for online video streaming based on 1080p HD video and 720p HD video. Using a one-to-one node network design, this article has offered an investigation of the performance of UDP in NS3. UDP experiences rather considerable packet losses during the simulation procedure by contrasting the pertinent performance indicators. The selection of the video streaming applications over UDP as an example relates the requirements for group video conferencing and the computation of packet sizes. Understanding the simulation's results can help network administrators deal with network problems involving video streaming, video conferencing, or any other applications that might be connected to the associated protocol.

5G‐based video summarization: An analysis from a methodological point of view

AbstractSurveillance is one of the fast‐growing applications used for monitoring and watching people, objects, or the environment to collect information and provide security. The surveillance data is in video form, and analyzing large video is challenging because it is essential to do efficient video streaming online. Video summarization comprises selecting, extracting, and aggregating keyframes for creating a synopsis, which is challenging. Though several methods have been proposed for video summarization, most are inconsistent, poor in processing and delivering video content, and do not focus on solving the root problems interlinked with efficient streaming. Thus, video streaming applications require an efficient video summarization model that can overcome existing issues and challenges and improve the overall quality of service integrated with the advanced technology of 5G. This paper has aimed to discuss various methods, approaches, and technologies used for video summarization to design a better model. It also presents various learning models and a taxonomy of available methods and provides a detailed review. The summary of the model used evaluates its outcome and the existing methods for potential future research works. The proposed approach is compared with existing ones to prove the model's efficiency. The result shows that the proposed model achieved a 62.3 and 52.3 F1 score summarizing the TVSum and SumMe datasets, respectively.

Dynamic Bitrate Adjustment in Web-based Video Streaming Applications Using HTTP Live Streaming (HLS)

Dynamic Bitrate Adjustment in Web-based Video Streaming Applications Using HTTP Live Streaming (HLS)

Content Specific Keyboard Layout Design for Smart Tv Applications

This research aims to propose a genetic algorithm based methodology to design optimum keyboard layout for video streaming applications designed for smart TVs. Users need to use the arrow buttons on their remote controls to type the title of the movie or the TV show they are searching for. We downloaded approximately 9 million movie and TV show titles from the Internet Movie Database (IMDB) website. After data preprocessing, we calculated the frequency of letter sequences in these titles and used genetic algorithms to determine the optimum keyboard layout that would minimize the total number of clicks required to type the title using arrow buttons on a remote control. The proposed keyboard layout is tested using the movie titles in IMBD Top 100 list and compared with standard QWERTY keyboard and three other keyboards in alphabetical order. It was confirmed that the proposed keyboard layout required fewer number of clicks than other keyboard layouts in 83 titles and tied with one of the other keyboards in 4 titles.

Real-Time Video Streaming Applications in Military 5G Networks: A Technical Review

Abstract: The integration of advanced telecommunications technologies into military operations marks a pivotal evolution in modern warfare strategies. With the advent of military-grade 5G networks, there arises an unprecedented capacity for real-time data streaming and enhanced operational transparency on the battlefield. This literature review critically examines the deployment of real-time streaming applications within such networks, focusing on a technology stack comprising React Native, Node.js, MongoDB, Docker, and WebRTC. These technologies collectively form the backbone of a system designed to leverage the full capabilities of 5G’s high throughput and ultra-low latency, essential for the demands of contemporary military engagements.

Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

SummaryWith the increase in internet connectivity worldwide, people have resorted to various video streaming applications for entertainment, education, healthcare, and so on. However, this has resulted in huge internet traffic, which is predominantly occurring due to the increased transmission of videos over wireless networks to mobile devices, Transmission of ultra‐high‐definition videos can be effortlessly carried out, although the video quality perceived by the users is normally lesser than the anticipated quality. In video streaming applications, it is a pre‐requisite to provide users with high quality of experience (QoE) but this is generally affected by the dynamic variations in the network conditions. This work presents a novel QoE‐based video delivery system in a collaborative e‐learning platform. Here, a novel deep learning structure recurrent neural network‐long short term memory (RNN‐LSTM) is developed for adaptive bit rate (ABR) selection, thereby providing users with videos of high QoE. Various network, time, and buffer‐based parameters are considered while selecting the bit rate. Additionally, the proposed RNN‐LSTM is assessed for its superiority in providing high QoE videos based on measures, like QoE, buffer size, and bit rate, and is observed to attain values of 0.975, 45.654, and 90.766 b/s, respectively.

Server Redundancy: Performa Jaringan Mengunakan DNS Failover MikroTik pada Kasus Private Server dan Public Server

The digitization of user services has increased, in line with the need for VPS and cloud computing services, which are rampant among application and platform developers. Quite a few companies that create applications or application users have servers to handle user needs. Testing is carried out using the ICMP Protocol to get real-time results and can be measured. From Scenario 1, carrying out 20 test requests, we get a packet loss of 5% with RTT Avarage of 195,838ms and Mdev 4,103ms. If you apply DNS failover in scenario 2, the client will likely access the web a little slower, as evidenced by the packet loss being 25% greater in value. Compared to scenario 1, having a high standard deviation (Mdev) of round-trip times is not desirable. This variation is also known as jitter. Increased jitter can cause a bad user experience, especially in real-time audio and video streaming applications. However, this is still understandable because it only has a 1-5 second effect on the service. Next, in scenario 3, we can see that private and public servers have relatively high gab with 0% packet loss, which has a small Mdev value of 0.309ms. Therefore, the DNS failover method is a solution for network administrators who have problems related to server migration between public servers and private servers so that services can run even if a server is maintaining or downlinking.

Performance evaluation of mpeg-5 part 2 (lcevc): impact of packet loss

This paper presents an evaluation of the latest MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) for video streaming applications using best effort protocols. LCEVC is a new video standard by MPEG, which enhances any base codec through an additional low bitrate stream, improving both video compression efficiency and and transmission. However, there is an interplay between packetization, packet loss visibility, choice of codec and video quality, which implies that prior studies with other codecs may be not as relevant. The contributions of this paper is, therefore in twofold: It evaluates the compression performance of LCEVC and then the impact of packet loss on its video quality when compared to H.264 and HEVC.The results from this evaluation suggest that, regarding compression, LCEVC outperformed its base codecs, overall in terms average encoding bitrate savings when using the constant rate factor (CRF) rate control. For example at a CRF of 19, the average encoding bitrate was reduced by 18.7% and 15.8% when compared with the base H.264 and HEVC codecs respectively. Furthermore, LCEVC produced better visual quality across the packet loss range compared to its base codecs and the quality only started to decrease once packet loss exceeded 0.8-1%, and decreases at a slower pace compared to its equivalent base codecs. This suggests that the LCEVC enhancement layer also provides error concealment. The results presented in this paper will be of interest to those considering the LCEVC standard and expected video quality in error-prone environments

A Multi-Channel Packet Scheduling Approach to Improving Video Delivery Performance in Vehicular Networks

When working with the Wireless Access in Vehicular Environment (WAVE) protocol stack, the multi-channel operation mechanism of the IEEE 1609.4 protocol may impact the overall network performance, especially when using video streaming applications. In general, packets delivered from the application layer during a Control Channel (CCH) time slot have to wait for transmission until the next Service Channel (SCH) time slot arrives. The accumulation of packets at the beginning of the latter time slot may introduce additional delays and higher contention when all the network nodes try, at the same time, to obtain access to the shared channel in order to send the delayed packets as soon as possible. In this work, we have analyzed these performance issues and proposed a new method, which we call SkipCCH, that helps the MAC layer to overcome the high contention produced by the packet transmission bursts at the beginning of every SCH slot. This high contention implies an increase in the number of packet losses, which directly impacts the overall network performance. With our proposal, streaming video in vehicular networks will provide a better quality of reconstructed video at the receiver side under the same network conditions. Furthermore, this method has particularly proven its benefits when working with Quality of Service (QoS) techniques, not only by increasing the received video quality but also because it avoids starvation of the lower-priority traffic.

Assessment of Optimization Parameters for Video Transmission over Optical Fiber using an H265/HEVC Encoder in Video Streaming Applications

Assessment of Optimization Parameters for Video Transmission over Optical Fiber using an H265/HEVC Encoder in Video Streaming Applications

A Hierarchical Multiscenario H.265/HEVC Video Encryption Scheme

With the pervasive application of video streaming, the security of streaming media information continually faces new challenges. Most existing encryption methods employ uniform criteria for encrypting all scenarios, leading to unnecessary mutual inhibition of algorithm security and coding efficiency. Furthermore, several encryption algorithms are inadequate for high-resolution videos. A novel, independently hierarchical video cryptosystem for H.265/high efficiency video coding (HEVC) is proposed that develops a scene-adaptive encryption strategy tailored for multiscenario videos. Additionally, we fully consider the seeds of pseudo-random number generators, syntax components in compressed codes, and scenario indicators. We analyze the visibility of encrypted videos in different scenarios and the encryption performance of various syntax parameters based on the integration of encryption for three distinct scenario categories to further enhance encryption efficiency and security. The method’s versatility is demonstrated using a diverse array of videos with significant and insignificant inter-frame motion information across varying resolutions. The experimental results from the video datasets indicate that our scheme effectively balances security and coding efficiency. Furthermore, the scene-adaptive approach can be tailored flexibly according to subscriber needs.

Demystifying the QoS and QoE of Edge-hosted Video Streaming Applications in the Wild with SNESet

Video streaming applications (VSAs) are increasingly being deployed on large-scale edge platforms, which have the potential to significantly improve the quality of service (QoS) and end-user experience (QoE), ultimately maximizing business outcomes. However, there is currently very little understanding of how QoS, QoE, and the impact of QoS on QoE for VSAs on edge platforms in the wild and at scale. To close the knowledge gap, we collect SNESet, an active measurement dataset comprising QoS and QoE telemetry metrics of 8 VSAs over four months, covering end-users from 798 edge sites,30 cities, and 3 ISPs in one country.We characterize and compare the QoS and QoE metrics in SNESet with existing publicly available datasets, highlighting that SNESet includes a significantly greater number of metrics (horizontal diversity and vertical hierarchy) and provides more comprehensive coverage of specific metrics.Moreover, we qualitatively and quantitatively analyze the impact of QoS on QoE in both domain-general and domain-specific scenarios. Our findings can inform the system design decisions that different entities in the video ecosystem (content providers, video player designers, third-party optimizers, edge vendors) make to maximize end-users experience and ultimately maximize the business outcomes. We hope SNESet can attract more research efforts in the data management community, computer network community, and beyond.

Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming Quality

Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate. By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.

DAMPAK PENGARUH PENGGUNAAN APLIKASI VIDEO LIVE STREAMING DI SMARTPHONE PADA KALANGAN PELAJAR

This study aims to determine the effect of using live streaming video applications that are currently popularly used by students. Live streaming video application itself is a video sharing application that is broadcast live and real time so that users can broadcast what activities are done and can be watched by everyone who has the same application and account. For interaction users can make two-way video calls with other users or interactions through the chat service provided. These applications are in the market such as Bigolive, Live.me, Ome.tv, Instagram live and others. This research was conducted using regression analysis. The analysis results obtained from this study that the use of live streaming video applications among students does have an impact on students such as students rarely attend religious activities, often make noise in class, often toy smartphones during lessons and even 51.58% talk dirty.

Optimal Resource Allocation for Loss-Tolerant Multicast Video Streaming.

In video streaming applications, especially during live streaming events, video traffic can account for a significant portion of the network traffic and can lead to severe network congestion. For such applications, multicast provides an efficient means to deliver the same content to a large number of users simultaneously. However, in multicast, if the base station transmits content at rates higher than what can be decoded by users with the worst channels, these users will experience outages. This makes the multicast system's performance dependent on the weakest users in the system. Interestingly, video streams can tolerate some packet loss without a significant degradation in the quality experienced by the users. This property can be leveraged to improve the multicast system's performance by reducing the dependence of the multicast transmissions on the weakest users. In this work, we design a loss-tolerant video multicasting system that allows for some controlled packet loss while satisfying the quality requirements of the users. In particular, we solve the resource allocation problem in a multimedia broadcast multicast services (MBMS) system by transforming it into the problem of stabilizing a virtual queuing system. We propose two loss-optimal policies and demonstrate their effectiveness using numerical examples with realistic traffic patterns from real video streams. It is shown that the proposed policies are able to keep the loss encountered by every user below its tolerable loss. The proposed policies are also able to achieve a significantly lower peak SNR degradation than the existing schemes.

Streaming From the Air : Enabling Drone-Sourced Video Streaming Applications on 5G Open-RAN Architectures

Enabling high data-rate uplink cellular connectivity for drones is a challenging problem, since a flying drone has a higher likelihood of having line-of-sight propagation to base stations that terrestrial UEs normally do not have line-of-sight to. This may result in uplink inter-cell interference and uplink performance degradation for the neighboring ground UEs when drones transmit at high data-rates (e.g., video streaming). We address this problem from a cellular operator's standpoint to support drone-sourced video streaming of a point of interest. We propose a low-complexity, closed-loop control system for Open-RAN architectures that jointly optimizes the drone's location in space and its transmission directionality to support video streaming and minimize its uplink interference impact on the network. We prototype and experimentally evaluate the proposed control system on a dedicated outdoor multi-cell RAN testbed, which is the first measurement campaign of its kind. Furthermore, we perform a large-scale simulation assessment of the proposed control system using the actual cell deployment topologies and cell load profiles of a major US cellular carrier. The proposed Open-RAN control scheme achieves an average 19% network capacity gain over traditional BS-constrained control solutions and satisfies the application data-rate requirements of the drone (e.g., to stream an HD video).

A Novel Strategy to Achieve Video Transcoding Using Cloud Computing

One of the fundamental challenges faced in deploying multimedia systems is delivering smooth and uninterrupted audio-visual information anywhere and anytime. In such systems, multimedia content is compressed within a certain format, this requires format conversion for various devices. Thus, a transcoding mechanism is required to make the content adaptive for various devices in the network. Video transcoding converts one digitally encoded format into another, this involves translating any file format containing video and audio at the same time. This is an essential feature for devices that do not support a specific format of media or have limited storage that requires a reduced file size. Through this paper, we provide a novel way of transcoding the block-based video coding schemes using cloud architecture by establishing a video pipelining architecture. The solution discussed in this paper would enable the end users to extract videos in any format and resolution seamlessly, combined with the scalability, reliability, and cost-effectiveness of the cloud. The proposed idea would be lucrative for all the video streaming applications that are currently relying on their legacy infrastructure for video transcoding.

Improving the performance of web-streaming by super-resolution upscaling techniques

In recent years, we have seen significant progress in advanced image upscaling techniques, sometimes called super-resolution, ML-based, or AI-based upscaling. Such algorithms are now available not only in form of specialized software but also in drivers and SDKs supplied with modern graphics cards. Upscaling functions in NVIDIA Maxine SDK is one of the recent examples. However, to take advantage of this functionality in video streaming applications, one needs to (a) quantify the impacts of super-resolution techniques on the perceived visual quality, (b) implement video rendering incorporating super-resolution upscaling techniques, and (c) implement new bitrate+resolution adaptation algorithms in streaming players, enabling such players to deliver better quality of experience or better efficiency (e.g. reduce bandwidth usage) or both. Towards this end, in this paper, we propose several techniques that may be helpful to the implementation community. First, we offer a model quantifying the impacts of super resolution upscaling on the perceived quality. Our model is based on the Westerink-Roufs model connecting the true resolution of images/videos to perceived quality, with several additional parameters added, allowing its tuning to specific implementations of super-resolution techniques. We verify this model by using several recent datasets including MOS scores measured for several conventional up-scaling and super-resolution algorithms. Then, we propose an improved adaptation logic for video streaming players, considering video bitrates, encoded video resolutions, player size, and the upscaling method. This improved logic relies on our modified Westerink-Roufs model to predict perceived quality and suggests choices of renditions that would deliver the best quality for given display and upscaling method characteristics. Finally, we study the impacts of the proposed techniques and show that they can deliver practically appreciable results in terms of the expected QoE improvements and bandwidth savings.

Low Latency Low Loss Media Delivery Utilizing In-Network Packet Wash

This paper presents new techniques and mechanisms for carrying streams of layered video using Scalable Video Coding (SVC) from servers to clients, utilizing the Packet Wash mechanism which is part of the Big Packet Protocol (BPP). BPP was designed to handle the transfer of packets for high-bandwidth, low-latency applications, aiming to overcome a number of issues current networks have with high precision services. One of the most important advantages of BPP is that it allows the dynamic adaption of packets during transmission. BPP uses Packet Wash to reduce the payload, and the size of a packet by eliminating specific chunks. For video, this means cutting out specific segments of the transferred video, rather than dropping packets, as happens with UDP based transmission, or retrying the transmission of packets, as happens with TCP. The chunk elimination approach is well matched with SVC video, and these techniques and mechanisms are utilized and presented. An evaluation of the performance is provided, plus a comparison of using UDP or TCP, which are the other common approaches for carrying media over IP. Our main contributions are the mapping of SVC video into BPP packets to provide low latency, low loss delivery, which provides better QoE performance than either UDP or TCP, when using those techniques and mechanisms. This approach has proved to be an effective way to enhance the performance of video streaming applications, by obtaining continuous delivery, while maintaining guaranteed quality at the receiver. In this work we have successfully used an H264 SVC encoded video for layered video transmission utilizing BPP, and can demonstrate video delivery with low latency and low loss in limited bandwidth environments.

Internet Video Delivery Improved by Super-Resolution with GAN

In recent years, image and video super-resolution have gained attention outside the computer vision community due to the outstanding results produced by applying deep-learning models to solve the super-resolution problem. These models have been used to improve the quality of videos and images. In the last decade, video-streaming applications have also become popular. Consequently, they have generated traffic with an increasing quantity of data in network infrastructures, which continues to grow, e.g., global video traffic is forecast to increase from 75% in 2017 to 82% in 2022. In this paper, we leverage the power of deep-learning-based super-resolution methods and implement a model for video super-resolution, which we call VSRGAN+. We train our model with a dataset proposed to teach systems for high-level visual comprehension tasks. We also test it on a large-scale JND-based coded video quality dataset containing 220 video clips with four different resolutions. Additionally, we propose a cloud video-delivery framework that uses video super-resolution. According to our findings, the VSRGAN+ model can reconstruct videos without perceptual distinction of the ground truth. Using this model with added compression can decrease the quantity of data delivered to surrogate servers in a cloud video-delivery framework. The traffic decrease reaches 98.42% in total.