Intelligent and Selective Video Frames Discarding Policies for Improving Video Quality over Wired/Wireless Networks

2013 ◽  
Author(s):  
Khalid A. Darabkh ◽  
Abeer M. Awad ◽  
Ala' F. Khalifeh
Keyword(s):  
Wireless Networks ◽  
Video Quality ◽  
Video Frames

scholarly journals Video steganography based on DCT psychovisual and object motion

2020 ◽  
Vol 9 (3) ◽  
pp. 1015-1023 ◽  
Author(s):  
Muhammad Fuad ◽  
Ferda Ernawan
Keyword(s):  
Video Quality ◽  
Object Motion ◽  
Multimedia Data ◽  
Secret Message ◽  
Hiding Capacity ◽  
Video Steganography ◽  
Limited Bandwidth ◽  
Video Frames ◽  
Dct Coefficients ◽  
Video Reconstruction

Steganography is a technique of concealing the message in multimedia data. Multimedia data, such as videos are often compressed to reduce the storage for limited bandwidth. The video provides additional hidden-space in the object motion of image sequences. This research proposes a video steganography scheme based on object motion and DCT-psychovisual for concealing the message. The proposed hiding technique embeds a secret message along the object motion of the video frames. Motion analysis is used to determine the embedding regions. The proposed scheme selects six DCT coefficients in the middle frequency using DCT-psychovisual effects of hiding messages. A message is embedded by modifying middle DCT coefficients using the proposed algorithm. The middle frequencies have a large hiding capacity and it relatively does not give significant effect to the video reconstruction. The performance of the proposed video steganography is evaluated in terms of video quality and robustness against MPEG compression. The experimental results produce minimum distortion of the video quality. Our scheme produces a robust of hiding messages against MPEG-4 compression with average NC value of 0.94. The proposed video steganography achieves less perceptual distortion to human eyes and it's resistant against reducing video storage.

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.

Cross-Layer Optimization for Video Transmission over WLAN

2012 ◽  
pp. 500-526
Author(s):  
Chih-Yu Wang ◽  
Yin-Cheng Huang ◽  
Cheng-Han Mai ◽  
Fu-Wang Chang ◽  
Hung-Yu Wei
Keyword(s):  
Video Coding ◽  
Video Quality ◽  
Cross Layer ◽  
Application Layer ◽  
Mac Layer ◽  
Medium Access ◽  
Access Delay ◽  
Video Frames ◽  
Wireless Medium ◽  
Advanced Video Coding

As IEEE 802.11 wireless devices have become increasingly widespread, providing Quality of Service in the context of H.264/AVC, the video coding standard for future multimedia networking, has become an important issue in the fields of communication and networking. Cross-Layer Adaptive Video Prioritization (CAVP) is a cross-layer framework that prioritizes video frame transmission according to the application-layer information and the MAC layer transmission condition. In this chapter, a Peak Signal-to-Noise Ratio (PSNR) estimation method is proposed to sort out different priorities of H.264/AVC (Advanced Video Coding) video frames at the application layer to provide user-centric media quality estimation. Compared to previous heuristic algorithms, the authors also investigate a theoretic access delay estimator to monitor the wireless medium access delay at the MAC layer. In addition, an admission control is employed to serve the delay-sensitive video application and to give higher priority to those critical video frames. Video packets are dynamically classified into different 802.11e access categories according to the level of wireless medium access delay and the priority of the video frames. The myths of naïvely prioritizing video packets based on I/P/B types as well as naïvely assign packets to high priority access categories in 802.11e are resolved. Rather than creating complex scheme that is unable to be implemented in practical scenarios, the authors design the proposed scheme with practical implementability in mind. The proposed scheme is implemented with Click kernel module and the MadWifi WLAN driver. The performance of proposed CAVP design is evaluated by both NS-2 simulations and real testbed experiments, and results show that it enhances receiving video quality in error-prone wireless networking environments.

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