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.

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 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.

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.

scholarly journals Using Factor Analysis Techniques to Find Out Objective Video Quality Metrics for Live Video Streaming over Cloud Mobile Media Services

2016 ◽  
Vol 8 (1) ◽  
pp. 126 ◽  
Author(s):  
Miguel Garcia-Pineda ◽  
Santiago Felici-Castell ◽  
Jaume Segura-Garcia
Keyword(s):  
Factor Analysis ◽  
Video Streaming ◽  
Video Quality ◽  
Mobile Media ◽  
Live Video ◽  
Analysis Techniques ◽  
Live Video Streaming ◽  
Streaming Applications ◽  
Opinion Score ◽  
Media Services

The increased adoption of smartphones, the access to mobile broadband networks and the availability of public Clouds allow new multimedia services, called Cloud Mobile Media Services. Under this new architecture the proliferation of live video streaming applications and the Quality of Experience (QoE) given by the final user are an issue, due to the higher and variable delay, as result of the virtualization methods used in the Clouds. Thus in this paradigm new challenges appear related to keep and estimate a good QoE in terms of a standarized subjective video quality called Mean Opinion Score (MOS). In this paper we analyze different approaches based on Factor Analysis techniques to estimate the subjective MOS both using Full Reference and Non Reference approaches. We compare the performance of the estimated MOS against publicly available video quality algorithms.

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.

Employing SDN to control video streaming applications in military mobile networks

2017 ◽  
Author(s):  
Iulisloi Zacarias ◽  
Janaina Schwarzrock ◽  
Luciano P. Gaspary ◽  
Anderson Kohl ◽  
Ricardo Q. A. Fernandes ◽  
...  
Keyword(s):  
Video Streaming ◽  
Mobile Networks ◽  
Streaming Applications

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

THE STUDY OF VEGETATION EFFECT ON WIRELESS STREAMING VIDEO FOR AGRICULTURAL MONITORING

2015 ◽  
Vol 76 (12) ◽  
Author(s):  
Mohd Fitri Ramli ◽  
Latifah Munirah Kamarudin ◽  
David Lorater Ndzi ◽  
Azizi Harun ◽  
Jamie Siregar Cynthia Turner ◽  
...  
Keyword(s):  
Video Streaming ◽  
Video Quality ◽  
Wireless Channel ◽  
Streaming Video ◽  
Signal Quality ◽  
Environmental Condition ◽  
Video File ◽  
Negative Effect ◽  
Wireless Video Streaming ◽  
Vegetation Effect

This paper presents the study of video streaming over wireless channel based on experimental measurements in the presence of fading caused by the physical environmental. The emulation of video streaming file through wireless channel is measured using IxChariot software from Ixia. The obtained emulation of signal quality from the video streaming file was measured in terms of network throughput, RSSI and packet loss. The results show the credibility of wireless network for streaming video file in agricultural area. The statistical results show that there is significant negative effect of physical environmental condition on wireless video streaming and received video quality.

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