Live Video Applications Research Articles

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.

Gratification of Yarmouk University Mass Communication Students’ Use of Mobile Phones

The aim of the current research is to study the uses of the mobile phone by students of the Faculty of Mass Communication at Yarmouk University as a means of communication and the gratifications achieved from it. The study adopts the survey method and is based on the two theories of media richness and digital transformation and it employes a questionnaire adminstered to a sample of 300 students of the Faculty of Mass Communication, Yarmouk University. The findings of the study show that the media students at Yarmouk University use their mobile phones intensively, and they use the mobile phone as a means of communication to a high degree. Further, the findings show that the use of the mobile phone satisfies the media students in a high degree, and it provides them with a means of entertainment, communication, and provides them with useful information. They use mobile social network applications to a high degree, the highest for live video applications, and the lowest for editing.

Toward Generalized Psychovisual Preprocessing For Video Encoding

Deep perceptual preprocessing has recently emerged as a new way to enable further bitrate savings across several generations of video encoders without breaking standards or requiring any changes in client devices. In this article, we lay the foundation for a generalized psychovisual preprocessing framework for video encoding and describe one of its promising instantiations that is practically deployable for video-on-demand, live, gaming, and user-generated content (UGC). Results using state-of-the-art advanced video coding (AVC), high efficiency video coding (HEVC), and versatile video coding (VVC) encoders show that average bitrate [Bjontegaard delta-rate (BD-rate)] gains of 11%–17% are obtained over three state-of-the-art reference-based quality metrics [Netflix video multi-method assessment fusion (VMAF), structural similarity index (SSIM), and Apple advanced video quality tool (AVQT)], as well as the recently proposed nonreference International Telecommunication Union-Telecommunication?(ITU-T) P.1204 metric. The proposed framework on CPU is shown to be twice faster than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 264$ </tex-math></inline-formula> medium-preset encoding. On GPU hardware, our approach achieves 714 frames/sec for 1080p video (below 2 ms/frame), thereby enabling its use in very-low-latency live video or game streaming applications.

Cratus: A Lightweight and Robust Approach for Mobile Live Streaming

Live video applications are getting popular, and content providers widely use adaptive bitrate (ABR) streaming to improve QoE while maintaining low latency. However, users’ increasing preference to watch videos on mobile devices poses great challenges for ABR algorithm due to the dramatically varying cellular network. Existing learn-based ABR algorithms face difficulties to generalize to various network conditions because of their reliance on training traces, and model/rule-based ABR schemes suffer from rebuffering under low latency constraint since they cannot robustly control the buffer occupancy within a small range. To address it, this work proposes Cratus, a lightweight and robust ABR algorithm for mobile live streaming, which achieves high QoE and low latency by accurately regulating the buffer at a small level. To enhance the control ability, Cratus controls the buffer dynamic behavior rather than the buffer occupancy. By using sliding mode control approach, Cratus robustly controls the buffer dynamic and ensures that the buffer occupancy is bounded around the target level regardless of network uncertainties. Trace-driven experiments show that Cratus outperforms existing ABRs: average QoE is increased by 12.3 to 28.6 percent, and rebuffering time is limited within 0.8 <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> on average, which is reduced by 53.5 to 92.3 percent.

Online Learning-Based Multi-Stage Complexity Control for Live Video Coding.

High Efficiency Video Coding (HEVC) can significantly improve the compression efficiency in comparison with the preceding H.264/Advanced Video Coding (AVC) but at the cost of extremely high computational complexity. Hence, it is challenging to realize live video applications on low-delay and power-constrained devices, such as the smart mobile devices. In this article, we propose an online learning-based multi-stage complexity control method for live video coding. The proposed method consists of three stages: multi-accuracy Coding Unit (CU) decision, multi-stage complexity allocation, and Coding Tree Unit (CTU) level complexity control. Consequently, the encoding complexity can be accurately controlled to correspond with the computing capability of the video-capable device by replacing the traditional brute-force search with the proposed algorithm, which properly determines the optimal CU size. Specifically, the multi-accuracy CU decision model is obtained by an online learning approach to accommodate the different characteristics of input videos. In addition, multi-stage complexity allocation is implemented to reasonably allocate the complexity budgets to each coding level. In order to achieve a good trade-off between complexity control and rate distortion (RD) performance, the CTU-level complexity control is proposed to select the optimal accuracy of the CU decision model. The experimental results show that the proposed algorithm can accurately control the coding complexity from 100% to 40%. Furthermore, the proposed algorithm outperforms the state-of-the-art algorithms in terms of both accuracy of complexity control and RD performance.

Direct and Semi-Direct Frame Partitioning Mapping for Computation and Energy-Aware HEVC Transrating

The most common operation to maintain various versions of the same video sequence under different bitrates in live and streaming video applications is video transrating. The typical transrating operation is comprised of a decoding and a complete encoding step in sequence, which results in long processing times and high energy consumption. This work proposes two early-termination schemes based on frame partitioning inheritance between decoding and encoding to accelerate the complex quadtree structure decisions performed during HEVC transrating. The proposed Direct Mapping scheme achieved time saving results of 48.5%, with a compression efficiency loss of 0.818%, on average. The Semi-Direct Mapping reaches 40.2% in time savings at the cost of an average compression efficiency loss of 0.724% in comparison to the original transcoder. Energy consumption was reduced in 50.4%, on average, considering the Direct Mapping approach.

Embedded Video Surveillance Based Moving Object Detection and Tracking

Background reckoning and the foreground, play prominent roles in the tasks of visual detection and tracking of objects. Moving Object Detection has been widely used in sundry discipline such as intelligent systems, security systems, video monitoring systems, banking places, provisionary systems, and so on. In this paper proposes moving objects detection and tracking method based on Embedded Video Surveillance. The method is based on using lines computed by a gradient-based optical flow and an edge detector gradient-based optical flow and edges are well matched for accurate computation of velocity, not much attention is paid to creating systems for tracking objects using this feature. The proposed method is compared with a recent work, proving its superior performance and when we want to represent high quality videos and images with, lower bit rate, and also suitable for real-world live video applications. This method reduces influences of foreground objects to the background model. The simulation results show that the background image can be obtained precisely and the moving objects recognition is achieved effectively.

Queuing Delay Model for Video Transmission Over Multi-Channel Underwater Wireless Optical Networks

In this paper, we analyze a delay-sensitive underwater wireless optical network used for livevideo applications. The video streams are generated at the sender and are transmitted through underwater multi-channel paths that span over several meters of length, where the receiver is subject to a maximum end-to-end delay constraint. We model this network with M/G/1 Markovian models to quantify the system's performance. Also, we obtain an approximate expression for the probability of blocking at the receiver considering an acceptable QoS. We provide an approximate expression for the minimum number of channels required to satisfy the preset QoS metric. Our model is validated using actual parameters from an existing setup and by comparing the analytical expressions for the end-to-end delay and probability of blocking for different scenarios with the simulation results.

APLIKASI 17 LIVE VIDEO STREAMING MENJADI SARANA AKTUALISASI DIRI BAGI GENERASI MILLENIAL

The development of advanced technology helps a lot towards an increasingly practical, efficient and dynamic of life. Social media has also become the choice of many people to be a practical social media. For millennia, social media is a very important thing. Many are enabled to have social media accounts, as these generations are using more social media. Applications 17 Live is one of the live streaming applications that really help users to make streaming video capture and then directly watched by many people without the process of downloading. To express their own existence, millennial generations often use live streaming video applications to upload them with the aim of attracting people's attention through videos they upload and becoming famous. This application is also one means of self-actualization.&#x0D; Keywords: Applications 17 Live Video Streaming, Self Actualization, Millennial Generation

FOS: A Funnel-Based Approach for Optimal Online Traffic Smoothing of Live Video

Traffic smoothing is an efficient means to reduce the bandwidth requirement for transmitting a variable-bit-rate video stream. Several traffic-smoothing algorithms have been presented to offline compute the transmission schedule for a prerecorded video. For live video applications, Sen present a sliding-window algorithm, referred to as SLWIN(k), to online compute the transmission schedule on the fly. SLWIN(k) looks ahead W video frames to compute the transmission schedule for the next k frametimes, where klesw. Note that W is upper bounded by the initial delay of the transmission. The time complexity of SLWIN(k) is O(W*N/k) for an N frame live video. In this paper, we present an O(N) online traffic-smoothing algorithm and two variants, denoted as FOS, FOS1 and FOS2, respectively. Note that O(N) is a trivial lower bound of the time complexity of the traffic-smoothing problem. Thus, the proposed algorithm is optimal. We compare the performance of our algorithms with SLWIN(k) based on several benchmark video clips. Experiment results show that FOS2, which adopts the aggressive workahead heuristic, further reduces the bandwidth requirement and better utilizes the client buffer for real-time interactive applications in which the initial delays are small

Online variable-bit-rate video traffic smoothing

The efficient transmission of variable-bit-rate (VBR) video streams is complicated by the burstiness that video compression standards such as MPEG introduce. Most of the existing techniques concentrate on stored video traffic smoothing or real-time video traffic smoothing. However, there is a growing number of live video applications, such as video-casts of courses or television news, where many clients may be willing to tolerate a playback delay of several seconds or minutes in exchange for a smaller throughput requirement. Bandwidth smoothing for these live video applications is referred to as online smoothing. In this paper, in order to measure the effectiveness of online video smoothing methods, we propose a benchmark algorithm, which provides an upper bound on some of the performance metrics in the smoothing results. Based on this algorithm, we found that significant discrepancy exists between the results produced by the existing online smoothing methods and the upper bound. With this observation, we focus on designing algorithms that can improve the smoothing results. Experimental results show that the proposed algorithms make considerable improvements compared to existing smoothing methods.

Adaptive traffic smoothing for live VBR MPEG video service

In this paper, we propose an online source traffic smoothing algorithm, that can be effectively used in live video applications such as visual lectures or television news. For a practical implementation, firstly, we describe the constraints imposed by the delay bound at the sender side, and by the sender/receiver buffer sizes. Then, by using these constraints, the proposed algorithm is designed in such a dynamic way as to smooth maximally the transmission rate. Through experimental results, it is shown that the proposed scheme is effective in reducing the peak rate, temporal variations, and effective bandwidth of a given video stream. Particularly, it is noted that the proposed algorithm can be used in some applications where overall buffer sizes are finite or asymmetrically located.