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.

Content-Based Video Streaming Approaches and Challenges

2011 ◽  
pp. 357-367 ◽  
Author(s):  
Ashraf M.A. Ahmad
Keyword(s):  
Video Streaming ◽  
Video Quality ◽  
Video Stream ◽  
Network Resource ◽  
Resource Requirements ◽  
Efficient Resource ◽  
Integrative Study ◽  
Integration Techniques ◽  
Content Processing ◽  
Processing Techniques

Video streaming poses significant technical challenges in quality of service guarantee and efficient resource management. Generally, it is recognized that end-to-end quality requirements of video streaming application can be reasonably achieved only by integrative study of advanced networking and content processing techniques. However, most existing integration techniques stop at the bit stream level, ignoring a deeper understanding of the media content. Yet, the underlying visual content of the video stream contains a vast amount of information that can be used to predict the bit-rate or quality more accurately. In the content-aware video streaming framework, video content is extracted automatically and used to control video quality under various manipulations and network resource requirements.

scholarly journals Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4172
Author(s):  
Frank Loh ◽  
Fabian Poignée ◽  
Florian Wamser ◽  
Ferdinand Leidinger ◽  
Tobias Hoßfeld
Keyword(s):  
Machine Learning ◽  
Video Streaming ◽  
Video Quality ◽  
Mobile App ◽  
Learning Approaches ◽  
Data Set ◽  
Adaptive Video Streaming ◽  
Adaptive Video ◽  
Quality Video ◽  
Current Monitoring

Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.

scholarly journals MDC adaptive video streaming

2021 ◽  
Author(s):  
Zi Ling
Keyword(s):  
Video Coding ◽  
Real Time ◽  
Video Streaming ◽  
Wireless Ad Hoc Networks ◽  
Error Concealment ◽  
Ad Hoc ◽  
Video Quality ◽  
Random Access ◽  
Current Status ◽  
Video Frame

Multiple Description Coding (MDC) is designed for multiple path video streaming with channel diversities. In this thesis, we investigate the performance of multi-path video streaming using the MDC technique. The MDC frame loss rate is one of the indicators of the real time video quality. A classification based framework for making mode decisions to minimize the MDC video frame transmission cost that may be defined in terms of the six parameters, number of sub-streams, number of transmission channels, GOP length, the I-frame positions, probability of network transmission states and probability of transmission changes. This thesis surveys the current status of horizontal decomposition into distributed computation, and vertical decomposition into functional modules such as congestion control, routing, scheduling, random access, and video coding. The focus of this thesis is on the video adaptive coding process to improve performance in terms of one or more of these factors. How to deliver a real-time MDC video from an end user over multi-channels is studied. The traffic is used to probe the network on determinig the network conditions and optimizing the coding algorithms appropriately. An efficient transmission statistical model Auto Regression (AR) to capture the properites of the region of interest is also introduced. Both the mode decisions and the error concealment require feedback from the network regarding the available bandwidth, loss probability, video coding methods and coding time spatial manners. The proposed algorithm works in a fully distributed environment, making it suitable for wireless ad hoc networks or other IP networks.

scholarly journals User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

2021 ◽  
Vol 18 (4(Suppl.)) ◽  
pp. 1387
Author(s):  
Muhamad Hanif Jofri ◽  
Ida Aryanie Bahrudin ◽  
Noor Zuraidin Mohd Safar ◽  
Juliana Mohamed ◽  
Abdul Halim Omar
Keyword(s):  
Video Streaming ◽  
User Satisfaction ◽  
Quality Of Experience ◽  
Video Quality ◽  
Video Content ◽  
Selection Scheme ◽  
Content Selection ◽  
Opinion Score ◽  
Quality Video

Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education,  and entertainment. However,   when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified into video attributes groups. The level of VCS streaming will gradually decrease to consider the least video selection that users will not accept depending on video quality. To evaluate the satisfaction level, we used the Mean Opinion Score (MOS) to measure the adaptability of user acceptance towards video streaming quality. The final results show that the proposed algorithm shows that the user satisfies the video selection, by altering the video attributes.

scholarly journals MDC adaptive video streaming

2021 ◽  
Author(s):  
Zi Ling
Keyword(s):  
Video Coding ◽  
Real Time ◽  
Video Streaming ◽  
Wireless Ad Hoc Networks ◽  
Error Concealment ◽  
Ad Hoc ◽  
Video Quality ◽  
Random Access ◽  
Current Status ◽  
Video Frame

Multiple Description Coding (MDC) is designed for multiple path video streaming with channel diversities. In this thesis, we investigate the performance of multi-path video streaming using the MDC technique. The MDC frame loss rate is one of the indicators of the real time video quality. A classification based framework for making mode decisions to minimize the MDC video frame transmission cost that may be defined in terms of the six parameters, number of sub-streams, number of transmission channels, GOP length, the I-frame positions, probability of network transmission states and probability of transmission changes. This thesis surveys the current status of horizontal decomposition into distributed computation, and vertical decomposition into functional modules such as congestion control, routing, scheduling, random access, and video coding. The focus of this thesis is on the video adaptive coding process to improve performance in terms of one or more of these factors. How to deliver a real-time MDC video from an end user over multi-channels is studied. The traffic is used to probe the network on determinig the network conditions and optimizing the coding algorithms appropriately. An efficient transmission statistical model Auto Regression (AR) to capture the properites of the region of interest is also introduced. Both the mode decisions and the error concealment require feedback from the network regarding the available bandwidth, loss probability, video coding methods and coding time spatial manners. The proposed algorithm works in a fully distributed environment, making it suitable for wireless ad hoc networks or other IP networks.

scholarly journals Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video

2021 ◽  
Vol 11 (11) ◽  
pp. 5270
Author(s):  
Waqas ur Rahman ◽  
Md Delowar Hossain ◽  
Eui-Nam Huh
Keyword(s):  
State Of The Art ◽  
Video Quality ◽  
Buffer Size ◽  
Bandwidth Utilization ◽  
Adaptation Algorithm ◽  
Control Laws ◽  
Quality Video ◽  
Video Player ◽  
High Quality Video ◽  
Abr Algorithm

Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, and HTTP clients offer distinct buffer sizes. The existing ABR algorithms use fixed control laws and are designed with predefined client/server settings. As a result, adaptation algorithms fail to achieve optimal performance across a variety of video client settings and QoE objectives. We propose a buffer- and segment-aware fuzzy-based ABR algorithm that selects video rates for future video segments based on segment duration and the client’s buffer size in addition to throughput and playback buffer level. We demonstrate that the proposed algorithm guarantees high QoE across various video player settings and video content characteristics. The proposed algorithm efficiently utilizes bandwidth in order to download high-quality video segments and to guarantee high QoE. The results from our experiments reveal that the proposed adaptation algorithm outperforms state-of-the-art algorithms, providing improvements in average video rate, QoE, and bandwidth utilization, respectively, of 5% to 18%, about 13% to 30%, and up to 45%.

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