Content-Based Video Quality Prediction for MPEG4 Video Streaming over Wireless Networks

2009 ◽  
Vol 4 (4) ◽  
Author(s):  
Asiya Khan ◽  
Lingfen Sun ◽  
Emmanuel Ifeachor
Keyword(s):  
Wireless Networks ◽  
Video Streaming ◽  
Video Quality ◽  
Quality Prediction

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 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 Video Transmission In Wireless Multihop Networks

2019 ◽  
Vol 8 (6) ◽  
pp. 3562-3565
Keyword(s):  
Wireless Networks ◽  
Wireless Network ◽  
Video Streaming ◽  
Video Transmission ◽  
Packet Scheduling ◽  
Video Quality ◽  
Video Communications ◽  
Wireless Data ◽  
Wireless Data Transmission ◽  
Wide Range

Multi hop wireless networks are being deployed in many video streaming applications because they have several potential features for next generation wireless communications. Though optimal encoding techniques offers significant quality retention in video transmission still it is insufficient to overcome the challenges ahead over wireless network transmission. In order to support wide range video communications in an efficient way certain Quality of service has to be retained in multi hop wireless network. To address this issue, this paper investigates several encoding and routing protocols video delivery over multi hop wireless networks. Specifically, we first investigate several encoding framework for videos and wireless data transmission over WMNs through individual paths; we then investigate the challenges ahead to formulate resistant routing model for least possible video quality dictions which incorporate channel status as well as the encoder properties over video characteristics. In this framework, routing techniques which can maximally used to achieve good video traffic with improved system performance. However, video streaming also have very stringent delay requirements, which makes it difficult to find optimal routes with the least possible distortions. To address this problem, we investigate several enhanced version of packet scheduling techniques for video communications over multi path multi hob multi user wireless network environment.

scholarly journals A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2851
Author(s):  
Obinna Izima ◽  
Ruairí Fréin ◽  
Ali Malik
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Video Streaming ◽  
Video Quality ◽  
Quality Prediction ◽  
Learning Techniques ◽  
Quality Of Delivery ◽  
Streaming Services ◽  
End To End

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.

QoS-based active dropping mechanism for NGN video streaming optimization

2014 ◽  
Vol 29 (4) ◽  
pp. 484-495 ◽  
Author(s):  
Chi-Yuan Chen ◽  
Tin-Yu Wu ◽  
Wei-Tsong Lee ◽  
Han-Chieh Chao ◽  
Jen-Chun Chiang
Keyword(s):  
Wireless Networks ◽  
Video Streaming ◽  
Scalable Video Coding ◽  
Video Quality ◽  
Buffer Management ◽  
Base Station ◽  
Effective Bandwidth ◽  
Bandwidth Utilization ◽  
Network Loading ◽  
The Impact

AbstractVideo streaming over mobile wireless networks is getting popular in recent years. High video quality relies on large bandwidth provisioning, however, it decreases the number of supported users in wireless networks. Thus, effective bandwidth utilization becomes a crucial issue in wireless network as the bandwidth resource in wireless environment is precious and limited. The NGN quality of service mechanisms should be designed to reduce the impact of traffic burstiness on buffer management. For this reason, we propose an active dropping mechanism to deal with the effective bandwidth utilization in this paper. We use scalable video coding extension of H.264/AVC standard to provide different video quality for users of different levels. In the proposed dropping mechanism, when the network loading exceeds the threshold, the dropping mechanism starts to drop data of the enhancement layers for users of low service level. The dropping probability alters according to the change in network loading. With the dropping mechanism, the base station increases the system capability and users are able to obtain better service quality when the system is under heavy loading. We also design several methods to adjust the threshold value dynamically. By using the proposed mechanism, better quality can be provided when the network is in congestion.

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