scholarly journals EVeREst: Bitrate Adaptation for Cloud VR

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 678
Author(s):  
Mikhail Liubogoshchev ◽  
Evgeny Korneev ◽  
Evgeny Khorov
Keyword(s):  
State Of The Art ◽  
Video Quality ◽  
Video Stream ◽  
Wireless Channel ◽  
The State ◽  
Video Frame ◽  
Live Streaming ◽  
Adaptation Algorithm ◽  
Remote Server

Cloud Virtual Reality (VR) technology is expected to promote VR by providing a higher Quality of Experience (QoE) and energy efficiency at lower prices for the consumer. In cloud VR, the virtual environment is rendered on the remote server and transmitted to the headset as a video stream. To guarantee real-time experience, networks need to transfer huge amounts of data with much stricter delays than imposed by the state-of-the-art live video streaming applications. To reduce the burden imposed on the networks, cloud VR applications shall adequately react to the changing network conditions, including the wireless channel fluctuations and highly variable user activity. For that, they need to adjust the quality of the video stream adaptively. This paper studies video quality adaptation for cloud VR and improves the QoE for cloud VR users. It develops a distributed, i.e., with no assistance from the network, bitrate adaptation algorithm for cloud VR, called the Enhanced VR bitrate Estimator (EVeREst). The algorithm aims to optimize the average bitrate of cloud VR video flows subject to video frame delay and loss constraints. For that, the algorithm estimates both the current network load and the delay experienced by separate frames. It anticipates the changes in the users’ activity and limits the bitrate accordingly, which helps prevent excess interruptions of the playback. With simulations, the paper shows that the developed algorithm significantly improves the QoE for the end-users compared to the state-of-the-art adaptation algorithms developed for MPEG DASH live streaming, e.g., BOLA. Unlike these algorithms, the developed algorithm satisfies the frame loss requirements of multiple VR sessions and increases the network goodput by up to 10 times.

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%.

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 Decentralised Approach to Intersection Traffic Management

2018 ◽  
Author(s):  
Huan Vu ◽  
Samir Aknine ◽  
Sarvapali D. Ramchurn
Keyword(s):  
Quality Of Life ◽  
Waiting Time ◽  
Traffic Congestion ◽  
Traffic Management ◽  
State Of The Art ◽  
The State ◽  
Management Mechanism ◽  
Traffic Conditions

Traffic congestion has a significant impact on quality of life and the economy. This paper presents a decentralised traffic management mechanism for intersections using a distributed constraint optimisation approach (DCOP). Our solution outperforms the state of the art solution both for stable traffic conditions (about 60% reduced waiting time) and robustness to unpredictable events. 

scholarly journals Smoothing vs. sharpening of colour images: Together or separated

2017 ◽  
Vol 2 (1) ◽  
pp. 299-316 ◽  
Author(s):  
Cristina Pérez-Benito ◽  
Samuel Morillas ◽  
Cristina Jordán ◽  
J. Alberto Conejero
Keyword(s):  
Image Denoising ◽  
Gaussian Distribution ◽  
State Of The Art ◽  
Visual Quality ◽  
The State ◽  
Art Methods ◽  
Efficiency And Effectiveness

AbstractIt is still a challenge to improve the efficiency and effectiveness of image denoising and enhancement methods. There exists denoising and enhancement methods that are able to improve visual quality of images. This is usually obtained by removing noise while sharpening details and improving edges contrast. Smoothing refers to the case of denoising when noise follows a Gaussian distribution.Both operations, smoothing noise and sharpening, have an opposite nature. Therefore, there are few approaches that simultaneously respond to both goals. We will review these methods and we will also provide a detailed study of the state-of-the-art methods that attack both problems in colour images, separately.

Streaming Coded Video in P2P Networks

2017 ◽  
pp. 188-222
Author(s):  
Muhammad Salman Raheel ◽  
Raad Raad
Keyword(s):  
Quality Of Service ◽  
Approximation Algorithms ◽  
Video Coding ◽  
State Of The Art ◽  
Scalable Video Coding ◽  
The State ◽  
P2p Network ◽  
Scalable Video ◽  
Adequate Quality

This chapter discusses the state of the art in dealing with the resource optimization problem for smooth delivery of video across a peer to peer (P2P) network. It further discusses the properties of using different video coding techniques such as Scalable Video Coding (SVC) and Multiple Descriptive Coding (MDC) to overcome the playback latency in multimedia streaming and maintains an adequate quality of service (QoS) among the users. The problem can be summarized as follows; Given that a video is requested by a peer in the network, what properties of SVC and MDC can be exploited to deliver the video with the highest quality, least upload bandwidth and least delay from all participating peers. However, the solution to these problems is known to be NP hard. Hence, this chapter presents the state of the art in approximation algorithms or techniques that have been proposed to overcome these issues.

The Playout Control Management

2011 ◽  
pp. 689-702
Author(s):  
Mirko Luca Lobina ◽  
Luigi Atzori ◽  
Fabrizio Boi
Keyword(s):  
Quality Of Service ◽  
Service Provider ◽  
Service Providers ◽  
State Of The Art ◽  
Main Idea ◽  
The State ◽  
Ip Telephony ◽  
Open Standard ◽  
Control Management

IP Telephony provides a way for an enterprise to extend consistent communication services to all employees, whether they are in main campus locations, at branch offices, or working remotely, also with a mobile phone. IP Telephony transmits voice communications over a network using open standard-based Internet protocols. This is both the strength and weakness of IP Telephony as the involved basic transport protocols (RTP, UDP, and IP) are not able to natively guarantee the required application quality of service (QoS). From the point of view of an IP Telephony Service Provider this definitely means possible waste of clients and money. Specifically the problem is at two different levels: i) in some countries, wherelong distance and particularly international call tariffs are high, perhaps due to a lack of competition or due to cross subsidies to other services, the major opportunity for IP Telephony Service Providers is for price arbitrage. This means working on diffusion of an acceptable service, although not at high quality levels; ii) in other countries, where different IP Telephony Service Providers already exist, the problem is competition for offering the best possible quality. The main idea behind this chapter is to analyze specifically the state of the art playout control strategies with the following aims: i) propose the reader the technical state of the art playout control management and planning strategies (overview of basic KPIs for IP Telephony); ii) compare the strategies IP Telephony Service Provider can choose with the aim of saving money and offering a better quality of service; iii) introduce also the state of the art quality index for IP Telephony, that is a set of algorithms for taking into account as many factors as possible to evaluate the service quality; iv) provide the reader with examples on some economic scenarios of IP Telephony.

scholarly journals A Fast 4K Video Frame Interpolation Using a Multi-Scale Optical Flow Reconstruction Network

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1251 ◽  
Author(s):  
Ahn ◽  
Jeong ◽  
Kim ◽  
Kwon ◽  
Yoo
Keyword(s):  
High Resolution ◽  
Optical Flow ◽  
State Of The Art ◽  
Interpolation Method ◽  
Video Frame ◽  
Frame Interpolation ◽  
Multi Scale ◽  
Reconstruction Scheme ◽  
Flow Reconstruction

Recently, video frame interpolation research developed with a convolutional neural network has shown remarkable results. However, these methods demand huge amounts of memory and run time for high-resolution videos, and are unable to process a 4K frame in a single pass. In this paper, we propose a fast 4K video frame interpolation method, based upon a multi-scale optical flow reconstruction scheme. The proposed method predicts low resolution bi-directional optical flow, and reconstructs it into high resolution. We also proposed consistency and multi-scale smoothness loss to enhance the quality of the predicted optical flow. Furthermore, we use adversarial loss to make the interpolated frame more seamless and natural. We demonstrated that the proposed method outperforms the existing state-of-the-art methods in quantitative evaluation, while it runs up to 4.39× faster than those methods for 4K videos.

scholarly journals Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning

2019 ◽  
Vol 33 ◽  
pp. 6722-6729 ◽  
Author(s):  
Ziming Li ◽  
Julia Kiseleva ◽  
Maarten De Rijke
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Imitation Learning ◽  
Local Optimum ◽  
Inverse Reinforcement Learning ◽  
High Quality ◽  
Overall Performance

The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator. The reward signal from a poor discriminator can be very sparse and unstable, which may lead the generator to fall into a local optimum or to produce nonsense replies. To alleviate the first problem, we first extend a recently proposed adversarial dialogue generation method to an adversarial imitation learning solution. Then, in the framework of adversarial inverse reinforcement learning, we propose a new reward model for dialogue generation that can provide a more accurate and precise reward signal for generator training. We evaluate the performance of the resulting model with automatic metrics and human evaluations in two annotation settings. Our experimental results demonstrate that our model can generate more high-quality responses and achieve higher overall performance than the state-of-the-art.

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

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