scholarly journals Improving Perceived Quality of Live Adaptative Video Streaming

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 948
Author(s):  
Carlos Eduardo Maffini Santos ◽  
Carlos Alexandre Gouvea da Silva ◽  
Carlos Marcelo Pedroso
Keyword(s):  
Video Streaming ◽  
Short Term Memory ◽  
Transmission Rate ◽  
Video Quality ◽  
Video On Demand ◽  
Live Streaming ◽  
Network Congestion ◽  
Congested Networks ◽  
Over The Top

Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.

scholarly journals Low Latency Live Streaming System with Congestion Control

2021 ◽  
Vol 9 (1) ◽  
pp. 8-13
Author(s):  
Toya Kinoshita ◽  
◽  
Hiroyuki Hisamatsu
Keyword(s):  
Congestion Control ◽  
Transmission Rate ◽  
Video Quality ◽  
Low Latency ◽  
Live Streaming ◽  
Network Congestion ◽  
Available Bandwidth ◽  
Congestion Control Mechanism ◽  
The Web

In recent years, the traffic for live streaming on the web has been increasing. The current live streaming methods that use MPEG-DASH or HLS are simple and scale easily to many clients using HTTP. However, they do not take into account the communication between the distributor and the viewer. As a result, latency between the distributor and the viewer is relatively high. Therefore, in this paper, we propose a low latency live streaming system on the web using WebRTC. Since WebRTC uses UDP, it does not have a congestion control mechanism. Depending on the network congestion, it is possible to stream video with quality that exceeds the available bandwidth. Therefore, we propose a system to change the video quality based on the congestion status. The proposed system increases or decreases the video transfer rate by changing the quality of the streamed video depending on the network conditions. We have evaluated the proposed system in a real network environment. As a result, we showed that the delay of the proposed system is smaller than that of the MPEG-DASH system. We also showed that the proposed system can change the quality of the video and switch the transmission rate appropriately according to the network conditions.

scholarly journals Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification

2019 ◽  
Vol 9 (11) ◽  
pp. 2297
Author(s):  
Kyeongseon Kim ◽  
Dohyun Kwon ◽  
Joongheon Kim ◽  
Aziz Mohaisen
Keyword(s):  
Video Streaming ◽  
Waiting Time ◽  
Quality Of Experience ◽  
Video Quality ◽  
Live Video ◽  
Streaming Services ◽  
Online Streaming ◽  
Over The Top ◽  
User Content

As the demand for over-the-top and online streaming services exponentially increases, many techniques for Quality of Experience (QoE) provisioning have been studied. Users can take actions (e.g., skipping) while streaming a video. Therefore, we should consider the viewing pattern of users rather than the network condition or video quality. In this context, we propose a proactive content-loading algorithm for improving per-user personalized preferences using multinomial softmax classification. Based on experimental results, the proposed algorithm has a personalized per-user content waiting time that is significantly lower than that of competing algorithms.

Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks

2019 ◽  
pp. 235-254
Author(s):  
Monalisa Ghosh ◽  
Chetna Singhal
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Video Streaming ◽  
Video Transmission ◽  
Video Quality ◽  
Subjective Quality ◽  
Perceptual Quality ◽  
Support Vector ◽  
Machine Learning Model

Video streaming services top the internet traffic surging forward a competitive environment to impart best quality of experience (QoE) to the users. The standard codecs utilized in video transmission systems eliminate the spatiotemporal redundancies in order to decrease the bandwidth requirement. This may adversely affect the perceptual quality of videos. To rate a video quality both subjective and objective parameters can be used. So, it is essential to construct frameworks which will measure integrity of video just like humans. This chapter focuses on application of machine learning to evaluate the QoE without requiring human efforts with higher accuracy of 86% and 91% employing the linear and support vector regression respectively. Machine learning model is developed to forecast the subjective quality of H.264 videos obtained after streaming through wireless networks from the subjective scores.

Adaptive Retransmission Scheme for Video Streaming Applications

2010 ◽  
pp. 248-262
Author(s):  
Árpád Huszák
Keyword(s):  
Video Streaming ◽  
Mobile Networks ◽  
Video Quality ◽  
Multimedia Applications ◽  
Limited Bandwidth ◽  
Streaming Applications ◽  
Congestion Control Algorithm ◽  
Negative Effect ◽  
Audio Video

In this chapter we present a novel selective retransmission scheme, based on congestion control algorithm. Our method is efficient in narrowband networks for multimedia applications, which demand higher bandwidth. Multimedia applications are becoming increasingly popular in IP networks, while in mobile networks the limited bandwidth and the higher error rate arise in spite of its popularity. These are restraining factors for mobile clients using multimedia applications such as video streaming. In some conditions the retransmission of lost and corrupted packets should increase the quality of the multimedia service, but these retransmissions should be enabled only if the network is not in congested state. Otherwise the retransmitted packet will intensify the congestion and it will have negative effect on the audio/video quality. Our proposed mechanism selectively retransmits the corrupted packets based on the actual video bit rate and the TCP-Friendly Rate Control (TFRC), which is integrated to the preferred DCCP transport protocol.

Impact of Wireless Channel Parameters on Quality of Video Streaming

1970 ◽  
Vol 108 (2) ◽  
pp. 27-30 ◽  
Author(s):  
S. Paulikas ◽  
P. Sargautis ◽  
V. Banevicius
Keyword(s):  
Video Streaming ◽  
Communication Systems ◽  
Video Quality ◽  
Human Perception ◽  
Wireless Channel ◽  
End User ◽  
Processing Power ◽  
Channel Parameters ◽  
Assess Quality

The problem of estimation of video quality obtained by end-user for mobile video streaming is addressed. Widely spreading mobile communication systems and increasing data transmission rates expand variety of multimedia services. One of such services is video streaming. So it is important to assess quality of this service. Consumers of video streaming are humans, and quality assessment must account human perception characteristics. Existing methods for user experienced video quality estimation as quality metrics usually usebit-error rate that has low correlation with by human perceived video quality. More advanced methods usually require too much processing power that cannot be obtained in handled mobile devices or intrusion into device firmware and/or hardware to obtain required data. However, recent research shows that channels throughput dedicated to some service (e.g. video streaming) can be tied to QoS perceived by an end-user indicator. This paper presents a research on impact of wireless channel parameters such as throughput and jitter on quality of video streaming. These wireless channel parameters can be easily obtained by monitoring IP level data streams in end-user’s device by fairly simple software agent for indication of video streaming QoS. Ill. 5, bibl. 10 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.108.2.138

scholarly journals Scalable Video Streaming for Single-Hop Wireless Networks Using a Contention-Based Access MAC Protocol

2008 ◽  
Vol 2008 ◽  
pp. 1-21
Author(s):  
Monchai Lertsutthiwong ◽  
Thinh Nguyen ◽  
Alan Fern
Keyword(s):  
Wireless Networks ◽  
Video Streaming ◽  
Admission Control ◽  
Packet Loss ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Mac Protocol ◽  
Video Quality ◽  
Streaming Applications

Limited bandwidth and high packet loss rate pose a serious challenge for video streaming applications over wireless networks. Even when packet loss is not present, the bandwidth fluctuation, as a result of an arbitrary number of active flows in an IEEE 802.11 network, can significantly degrade the video quality. This paper aims to enhance the quality of video streaming applications in wireless home networks via a joint optimization of video layer-allocation technique, admission control algorithm, and medium access control (MAC) protocol. Using an Aloha-like MAC protocol, we propose a novel admission control framework, which can be viewed as an optimization problem that maximizes the average quality of admitted videos, given a specified minimum video quality for each flow. We present some hardness results for the optimization problem under various conditions and propose some heuristic algorithms for finding a good solution. In particular, we show that a simple greedy layer-allocation algorithm can perform reasonably well, although it is typically not optimal. Consequently, we present a more expensive heuristic algorithm that guarantees to approximate the optimal solution within a constant factor. Simulation results demonstrate that our proposed framework can improve the video quality up to 26% as compared to those of the existing approaches.

scholarly journals PENERAPAN METODE HTB DAN DIFFSERV GUNA PENINGKATAN QOS PADA LAYANAN VIDEO STREAMING

2019 ◽  
Vol 9 (3) ◽  
pp. 35-40
Author(s):  
Mitra Unik ◽  
Soni Soni ◽  
Randra Aguslan Pratama
Keyword(s):  
Quality Of Service ◽  
Video Streaming ◽  
Packet Loss ◽  
Digital Video ◽  
Streaming Media ◽  
Fundamental Problem ◽  
Streaming Video ◽  
Live Streaming ◽  
Delay Jitter

Abstract One of the popular internet services in use today is video streaming, either live (live streaming) or pre-recorder. Streaming video is a type of streaming media where data from video files is continuously transmitted over the internet to remote users. This fundamental problem appears to be influenced by the biggest factor which is the limited infrastructure of network resources which causes poor video quality. The process of digital video communication is known to consume quite a large resource, because in general the bandwidth requirements for sending Video and Audio signals. To maintain the quality of the video being played, there are several instruments needed, one of which is a data connection that is required to have Quality of Service (QoS). The parameters used in the measurement of QoS are delay, jitter, packet loss, throughput. This study uses the PPDIO method as a workflow with a Network Lifecycle approach. In this research, there are many factors that influence the quality of video, namely network factors and hardware factors. The test results obtained are not absolute, so it is possible that there will be differences in subsequent testing. Encoding also affects the quality of the video. Bandwidth equalization according to priority when the traffic conditions of all packets are full. Based on a comparative analysis of QoS parameter calculations using HTB and Diffserv methods, a comparison of throughput, jitter and delay does not differ greatly between clients. Keywords: Video Streaming, Diffserv, HTB, QoS Abstrak Salah satu layanan dari internet yang populer digunakan saat ini adalah video streaming, baik secara langsung (live streaming) atau pre-recorder. Streaming video merupakan jenis streaming media dimana data dari file video secara terus menerus dikirimkan melalui jaringan internet ke pengguna jarak jauh. Permasalahan mendasar ini muncul dipengaruhi oleh faktor terbesar yaitu terbatasnya infrastruktur sumber daya jaringan yang menyebabkan kualitas video yang buruk. Proses  komunikasi  digital  video,  diketahui  menghabiskan  resource  yang  cukup  besar, dikarenakan Secara umum kebutuhan bandwidth untuk mengirimkan sinyal Video dan Audio. Guna menjaga kualitas dari video yang dimainkan, terdapat beberapa instrument yang dibutuhkan, salah satunya adalah koneksi data yang wajib memiliki Quality of Service (QoS). Adapun Parameter yang digunakan dalam pengukuran QoS adalah delay, jitter, packet loss, Throughput. Penelitian ini menggunakan metode PPDIO sebagai alur kerja dengan pendekatan Network Lifecycle. Pada penelitian ini didapat Banyak faktor yang mempengaruhi kualitas dari video yaitu faktor jaringan dan faktor dari Hardware. Hasil pengujian didapat tidaklah mutlak sehingga tidak menutup kemungkinan akan ada perbedaan pada pengujian selanjutnya. Encoding juga mempengaruhi kualitas dari video. pemerataan Bandwidth sesuai prioritasnya saat kondisi traffic seluruh paket penuh. Berdasarkan analisa perbandingan perhitungan parameter QoS menggunakan metode HTB dan Diffserv, didapatkan  perbandingan troughput, jitter dan delay yang tidak berbeda jauh antara klien. Kata kunci: Video streaming, Diffserv, HTB, QoS  

scholarly journals REAL-TIME INTERACTIVE MECHANISM FOR LIVE STREAMING ON PEER-TO-PEER NETWORKS

2020 ◽  
pp. 208-215
Author(s):  
Mina N. Abadeer ◽  
Rowayda A. Sadek ◽  
Gamal I. Selim
Keyword(s):  
Real Time ◽  
Video Streaming ◽  
Packet Delay ◽  
Peer To Peer ◽  
Live Streaming ◽  
P2p Streaming ◽  
Neighbor Selection ◽  
Streaming Technology ◽  
Selection Methodology

Quality of live video streaming technology is based on quality of Experiences parameters (QoE). Approaching the peer-to-peer (P2P) or peer-assisted networks as a sympathetic solution is highly required, especially in light of its authentic scalability and its extremely low initial cost requirements. However, the design of robust, efficient, and performing P2P streaming systems remains a high challenge when real-time constraints are part of the quality of service (QoS), as in TV distribution or conferencing applications. One of the P2P main issues that affect the quality of streaming is the neighbor selection methodology. The proposed work presents an effective mesh-based neighbor selection approaches for video streaming – Uniform Peer Distribution Algorithm (UPDA) – based on QoS and QoE Parameters. UPDA shortens the latency to be ranging from 10 ms to 50 ms servicing up to 4000 online peers under failure / recovery tests. Simulation results demonstrate that the proposed UPDA achieves good performance in End-to End delay with a percentage of 10.4 % and packet delay variation about 2% compared to random neighbor selection method.

scholarly journals EVeREst: Bitrate Adaptation for Cloud VR

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 678
Author(s):  
Mikhail Liubogoshchev ◽  
Evgeny Korneev ◽  
Evgeny Khorov
Keyword(s):  
State Of The Art ◽  
Video Quality ◽  
Video Stream ◽  
Wireless Channel ◽  
The State ◽  
Video Frame ◽  
Live Streaming ◽  
Adaptation Algorithm ◽  
Remote Server

Cloud Virtual Reality (VR) technology is expected to promote VR by providing a higher Quality of Experience (QoE) and energy efficiency at lower prices for the consumer. In cloud VR, the virtual environment is rendered on the remote server and transmitted to the headset as a video stream. To guarantee real-time experience, networks need to transfer huge amounts of data with much stricter delays than imposed by the state-of-the-art live video streaming applications. To reduce the burden imposed on the networks, cloud VR applications shall adequately react to the changing network conditions, including the wireless channel fluctuations and highly variable user activity. For that, they need to adjust the quality of the video stream adaptively. This paper studies video quality adaptation for cloud VR and improves the QoE for cloud VR users. It develops a distributed, i.e., with no assistance from the network, bitrate adaptation algorithm for cloud VR, called the Enhanced VR bitrate Estimator (EVeREst). The algorithm aims to optimize the average bitrate of cloud VR video flows subject to video frame delay and loss constraints. For that, the algorithm estimates both the current network load and the delay experienced by separate frames. It anticipates the changes in the users’ activity and limits the bitrate accordingly, which helps prevent excess interruptions of the playback. With simulations, the paper shows that the developed algorithm significantly improves the QoE for the end-users compared to the state-of-the-art adaptation algorithms developed for MPEG DASH live streaming, e.g., BOLA. Unlike these algorithms, the developed algorithm satisfies the frame loss requirements of multiple VR sessions and increases the network goodput by up to 10 times.

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