VidCloud

2021 ◽  
Vol 5 (4) ◽  
pp. 1-32
Author(s):  
Abubakr O. Al-Abbasi ◽  
Vaneet Aggarwal
Keyword(s):  
Video Streaming ◽  
Distributed Storage ◽  
Convex Combination ◽  
Video Quality ◽  
Moment Generating Function ◽  
Content Delivery Network ◽  
Two Stage ◽  
Erasure Code ◽  
Probabilistic Scheduling ◽  
Parallel Streams

As video-streaming services have expanded and improved, cloud-based video has evolved into a necessary feature of any successful business for reaching internal and external audiences. In this article, video streaming over distributed storage is considered where the video segments are encoded using an erasure code for better reliability. We consider a representative system architecture for a realistic (typical) content delivery network (CDN). Given multiple parallel streams/link between each server and the edge router, we need to determine, for each client request, the subset of servers to stream the video, as well as one of the parallel streams from each chosen server. To have this scheduling, this article proposes a two-stage probabilistic scheduling. The selection of video quality is also chosen with a certain probability distribution that is optimized in our algorithm. With these parameters, the playback time of video segments is determined by characterizing the download time of each coded chunk for each video segment. Using the playback times, a bound on the moment generating function of the stall duration is used to bound the mean stall duration. Based on this, we formulate an optimization problem to jointly optimize the convex combination of mean stall duration and average video quality for all requests, where the two-stage probabilistic scheduling, video quality selection, bandwidth split among parallel streams, and auxiliary bound parameters can be chosen. This non-convex problem is solved using an efficient iterative algorithm. Based on the offline version of our proposed algorithm, an online policy is developed where servers selection, quality, bandwidth split, and parallel streams are selected in an online manner. Experimental results show significant improvement in QoE metrics for cloud-based video as compared to the considered baselines.

scholarly journals Robust Video Communication over an Urban VANET

2010 ◽  
Vol 6 (3) ◽  
pp. 259-280 ◽  
Author(s):  
N. Qadri ◽  
M. Altaf ◽  
M. Fleury ◽  
M. Ghanbari
Keyword(s):  
Video Streaming ◽  
Ad Hoc ◽  
Source Coding ◽  
Video Quality ◽  
Video Communication ◽  
Network Resource ◽  
Multiple Path ◽  
Quality Video ◽  
Considerable Benefit ◽  
Redundancy Coding

Video communication within a Vehicular Ad Hoc Network (VANET) has the potential to be of considerable benefit in an urban emergency, as it allows emergency vehicles approaching the scene to better understand the nature of the emergency. However, the lack of centralized routing and network resource management within a VANET is an impediment to video streaming. To overcome these problems the paper pioneers source-coding techniques for VANET video streaming. The paper firstly investigates two practical multiple-path schemes, Video Redundancy Coding (VRC) and the H.264/AVC codec's redundant frames. The VRC scheme is reinforced by gradual decoder refresh to improve the delivered video quality. Evaluation shows that multiple-path 'redundant frames' achieves acceptable video quality at some destinations, whereas VRC is insufficient. The paper also demonstrates a third source coding scheme, single-path streaming with Flexible Macroblock Ordering, which is also capable of delivery of reasonable quality video. Therefore, video communication between vehicles is indeed shown to be feasible in an urban emergency if the suitable source coding techniques are selected.

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 A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming

2021 ◽  
Vol 27 (1) ◽  
pp. 112-129
Author(s):  
Saba Qasim Jabbar ◽  
Dheyaa Jasim Kadhim
Keyword(s):  
Video Streaming ◽  
Video Quality ◽  
Prediction Algorithm ◽  
Buffer Size ◽  
Video Playback ◽  
Buffer Occupancy ◽  
Simulation Results ◽  
Streaming Technology ◽  
The Impact ◽  
Buffer Structure

A robust video-bitrate adaptive scheme at client-aspect plays a significant role in keeping a good quality of video streaming technology experience. Video quality affects the amount of time the video has turned off playing due to the unfilled buffer state. Therefore to maintain a video streaming continuously with smooth bandwidth fluctuation, a video buffer structure based on adapting the video bitrate is considered in this work. Initially, the video buffer structure is formulated as an optimal control-theoretic problem that combines both video bitrate and video buffer feedback signals. While protecting the video buffer occupancy from exceeding the limited operating level can provide continuous video streaming, it may also cause a video bitrate oscillation. So the video buffer structure is adjusted by adding two thresholds as operating points for overflow and underflow states to filter the impact of throughput fluctuation on video buffer occupancy level. Then a bandwidth prediction algorithm is proposed for enhancing the performance of video bitrate adaptation. This algorithm's work depends on the current video buffer level, video bitrate of the previous segment, and iterative throughput measurements to predict the best video bitrate for the next segment. Simulation results show that reserving a bandwidth margin is better in adapting the video bitrate under bandwidth variation and then reducing the risk of video playback freezing. Simulation results proved that the playback freezing happens two times: firstly, when there is no bandwidth margin used and secondly, when the bandwidth margin is high while smooth video bitrate is obtained with moderate value. The proposed scheme is compared with other two schemes such as smoothed throughput rate (STR) and Buffer Based Rate (BBR) in terms of prediction error, QoE preferences, buffer size, and startup delay time, then the proposed scheme outperforms these schemes in attaining smooth video bitrates and continuous video playback.

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.

Wireless Multimedia Content Distribution Architecture

2012 ◽  
pp. 78-104
Author(s):  
Israel Pérez-Llopis ◽  
Carlos E. Palau ◽  
Manuel Esteve
Keyword(s):  
Video Streaming ◽  
Access Networks ◽  
Wireless Access ◽  
Wireless Access Networks ◽  
Wireless Multimedia ◽  
Content Delivery Network ◽  
Wireless Environment ◽  
Always Best Connected ◽  
Individual Client ◽  
Wireless Video Streaming

Wireless video streaming, and specifically IPTV, has been a key challenge during the last decade, including the provision of access to users on an always best connected basis using different wireless access networks, including continuous seamless mobility. There are different proposals including IP based video streaming, DVB-H, or MediaFLO to carry out IPTV and video streaming on demand to users in a wireless environment, but one of the most relevant elements is the architecture of the service, with all the components of the delivery process. In this work the authors propose an alternative architecture based on a wireless Content Delivery Network, optimized to distribute video to mobile terminals in order to create a triple screen platform; considering that the main available wireless access networks are WiFi, WiMAX, and 3G, this work focuses on the last two. Surrogates within the CDN architecture act as video streaming servers, while the origin servers in the content providers carry out the transcoding process in order to be compliant with individual client requirements.

Impact of Cross-Layer Adaptations of Mobile IP on IEEE 802.11 Networks on Video Streaming

2010 ◽  
Vol 1 (3) ◽  
pp. 59-80
Author(s):  
P. De Cleyn ◽  
C. Blondia
Keyword(s):  
Video Streaming ◽  
Mobile Ip ◽  
Video Quality ◽  
Real Life ◽  
Cross Layer ◽  
Performance Gain ◽  
Layer Model ◽  
802.11 Networks ◽  
Simulation Scenario ◽  
Cross Layering

The OSI network layer model provides a strictly separated stacked architecture to abstract the behavior of one layer from the other. Although this model has a lot of advantages, it also makes it easy to lose the bigger picture. In this paper, the authors describe the advantages that can be made by cross-layering the link layer and networking layer to optimize handovers. The performance gain of these cross-layer adaptations will be analyzed using a simulation scenario and compared to the results from a real-life video streaming test. The authors will show that the performance gain in network parameters cannot be directly mapped on the gain of the video quality.

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.

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