scholarly journals Fuzzy Logic-Based Adaptive Point Cloud Video Streaming

2020 ◽  
Author(s):  
Zhi Liu ◽  
Jie Li ◽  
Xianfu Chen ◽  
Celimuge Wu ◽  
susumu ishihara ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Video Streaming ◽  
Point Cloud ◽  
Degrees Of Freedom ◽  
Video Quality ◽  
Base Layer ◽  
Quality Level ◽  
Real Point ◽  
Network Bandwidth ◽  
Video Streaming Scheme

Point cloud video provides 6 degrees of freedom (6DoF) viewing experiences to allow users to freely select the viewing angles of 3D scenes and is expected to be the next-generation video. This paper studies the point cloud video streaming and proposes a fuzzy logic-based point cloud video streaming scheme to solve the inherent technical issues. In particular, a point cloud video is first partitioned into smaller tiles, along with a low-quality base layer covering the entire video. Each tile is encoded into different quality levels, and both the compressed and uncompressed (i.e., decoded) versions of each tile are prepared for selection. Then, based on the user’s viewing angle and predicted future network bandwidth condition, fuzzy logic empowered quality level selection, with properly defined novel fuzzification, fuzzy rules, and defuzzification, is conducted to maximize the received point cloud video quality under the communication resource, computational resource and quality requirements constraints. Extensive simulations based on real point cloud video sequences and network traces are conducted, and the results reveal the superiority of the proposed scheme over the baseline scheme. To the best of our knowledge, this is the first work studying point cloud video streaming using fuzzy logic. <br>

scholarly journals Fuzzy Logic-Based Adaptive Point Cloud Video Streaming

2020 ◽  
Author(s):  
Zhi Liu ◽  
Jie Li ◽  
Xianfu Chen ◽  
Celimuge Wu ◽  
susumu ishihara ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Video Streaming ◽  
Point Cloud ◽  
Degrees Of Freedom ◽  
Video Quality ◽  
Base Layer ◽  
Quality Level ◽  
Real Point ◽  
Network Bandwidth ◽  
Video Streaming Scheme

Point cloud video provides 6 degrees of freedom (6DoF) viewing experiences to allow users to freely select the viewing angles of 3D scenes and is expected to be the next-generation video. This paper studies the point cloud video streaming and proposes a fuzzy logic-based point cloud video streaming scheme to solve the inherent technical issues. In particular, a point cloud video is first partitioned into smaller tiles, along with a low-quality base layer covering the entire video. Each tile is encoded into different quality levels, and both the compressed and uncompressed (i.e., decoded) versions of each tile are prepared for selection. Then, based on the user’s viewing angle and predicted future network bandwidth condition, fuzzy logic empowered quality level selection, with properly defined novel fuzzification, fuzzy rules, and defuzzification, is conducted to maximize the received point cloud video quality under the communication resource, computational resource and quality requirements constraints. Extensive simulations based on real point cloud video sequences and network traces are conducted, and the results reveal the superiority of the proposed scheme over the baseline scheme. To the best of our knowledge, this is the first work studying point cloud video streaming using fuzzy logic. <br>

scholarly journals Fuzzy Logic-Based Adaptive Point Cloud Video Streaming

2020 ◽  
Vol 1 ◽  
pp. 121-130
Author(s):  
Zhi Liu ◽  
Jie Li ◽  
Xianfu Chen ◽  
Celimuge Wu ◽  
Susumu Ishihara ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Video Streaming ◽  
Point Cloud

scholarly journals Region of interest–based segmented tiled adaptive streaming using head-mounted display tracking sensing data

2019 ◽  
Vol 15 (12) ◽  
pp. 155014771989453 ◽  
Author(s):  
Hyun-Wook Kim ◽  
Sung-Hyun Yang
Keyword(s):  
Virtual Reality ◽  
Video Streaming ◽  
Region Of Interest ◽  
Base Layer ◽  
Adaptive Streaming ◽  
High Quality ◽  
Head Mounted Display ◽  
Network Bandwidth ◽  
Quality Video ◽  
High Quality Video

To support 360 virtual reality video streaming services, high resolutions of over 8K and network streaming technology that guarantees consistent quality of service are required. To this end, we propose 360 virtual reality video player technology and a streaming protocol based on MPEG Dynamic Adaptive Streaming over HTTP Spatial Representation Description to support the player. The player renders the downsized video as the base layer, which has a quarter of the resolution of the original video, and high-quality video tiles consisting of tiles obtained from the tiled-encoded high-quality video (over 16K resolution) as the enhanced layer. Furthermore, we implemented the system and conducted experiments to measure the network bandwidth for 16K video streaming and switching latency arising from changes in the viewport. From the results, we confirmed that the player has a switching latency of less than 1000 ms and a maximum network download bandwidth requirement of 100 Mbps.

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.

A Video-Quality Driven Strategy in Short Video Streaming

2021 ◽  
Author(s):  
Jing Guo ◽  
Guanghui Zhang
Keyword(s):  
Video Streaming ◽  
Video Quality ◽  
Short Video
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