scholarly journals Adaptive Streaming of Scalable Videos over P2PTV

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Youssef Lahbabi ◽  
El Hassan Ibn Elhaj ◽  
Ahmed Hammouch
Keyword(s):  
Video Coding ◽  
Quality Of Experience ◽  
Scalable Video Coding ◽  
Video Quality ◽  
The Other ◽  
Scalable Video ◽  
Adaptive Streaming ◽  
Network State ◽  
Adaptation Mechanisms

In this paper, we propose a new Scalable Video Coding (SVC) quality-adaptive peer-to-peer television (P2PTV) system executed at the peers and at the network. The quality adaptation mechanisms are developed as follows: on one hand, the Layer Level Initialization (LLI) is used for adapting the video quality with the static resources at the peers in order to avoid long startup times. On the other hand, the Layer Level Adjustment (LLA) is invoked periodically to adjust the SVC layer to the fluctuation of the network conditions with the aim of predicting the possible stalls before their occurrence. Our results demonstrate that our mechanisms allow quickly adapting the video quality to various system changes while providing best Quality of Experience (QoE) that matches current resources of the peer devices and instantaneous throughput available at the network state.

scholarly journals Multipath Dynamic Adaptive Streaming over HTTP Using Scalable Video Coding in Software Defined Networking

2020 ◽  
Vol 10 (21) ◽  
pp. 7691
Author(s):  
Ali Gohar ◽  
Sanghwan Lee
Keyword(s):  
Video Coding ◽  
Network Topology ◽  
Service Providers ◽  
Scalable Video Coding ◽  
Software Defined Networking ◽  
Multimedia Streaming ◽  
Scalable Video ◽  
Adaptive Streaming ◽  
Network Bandwidth

Dynamic Adaptive Streaming over HTTP (DASH) offers adaptive and dynamic multimedia streaming solutions to heterogeneous end systems. However, it still faces many challenges in determining an appropriate rate adaptation technique to provide the best quality of experience (QoE) to the end systems. Most of the suggested approaches rely on servers or client-side heuristics to improve multimedia streaming QoE. Moreover, using evolving technologies such as Software Defined Networking (SDN) that provide a network overview, combined with Multipath Transmission Control Protocol (MPTCP), can enhance the QoE of streaming multimedia media based on scalable video coding (SVC). Therefore, we enhance our previous work and propose a Dynamic Multi Path Finder (DMPF) scheduler that determines optimal techniques to enhance QoE. DMPF scheduler is a part of the DMPF Scheduler Module (DSM) which runs as an application over the SDN controller. The DMPF scheduler accommodates maximum client requests while providing the basic representation of the media requested. We evaluate our implementation on real network topology and explore how SVC layers should be transferred over network topology. We also test the scheduler for network bandwidth usage. Through extensive simulations, we show clear trade-offs between the number of accommodated requests and the quality of the streaming. We conclude that it is better to schedule the layers of a request into the same path as much as possible than into multiple paths. Furthermore, these result would help service providers optimize their services.

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.

Scalable Video Delivery over Wireless LANs

2012 ◽  
pp. 429-465
Author(s):  
Maodong Li ◽  
Seong-Ping Chuah ◽  
Zhenzhong Chen ◽  
Yap-Peng Tan
Keyword(s):  
Quality Of Service ◽  
Video Coding ◽  
System Performance ◽  
Scalable Video Coding ◽  
Local Area ◽  
Scalable Video ◽  
Cross Layer Design ◽  
Wireless Broadband ◽  
Related Quality

Recent advances in wireless broadband networks and video coding techniques have led the rapid growth of wireless video services. In this chapter, we present a comprehensive study on the transmission of scalable video over wireless local area networks (WLAN). We analyze first the mechanisms and principles of the emerging scalable video coding (SVC) standard. We then introduce the IEEE 802.11 standards for WLAN and related quality of service (QoS) issues. We present some studies of SVC over WLAN using cross-layer design techniques. We aim to exploit the unique characteristics of the scalable video coding technology to enhance personalized experience and to improve system performance in a wireless transmission system. Examples and analyses are given to demonstrate system performances.

scholarly journals P2P video streaming combining SVC and MDC

2011 ◽  
Vol 21 (2) ◽  
pp. 295-306 ◽  
Author(s):  
Francisco de Asís López-Fuentes
Keyword(s):  
Video Coding ◽  
Video Streaming ◽  
Packet Loss ◽  
Scalable Video Coding ◽  
Video Quality ◽  
Scalable Video ◽  
Video Content ◽  
Multiple Description ◽  
Video Distribution ◽  
P2p Video Streaming

P2P video streaming combining SVC and MDC In this paper we propose and evaluate a combined SVC-MDC (Scalable Video Coding & Multiple Description Video Coding) video coding scheme for Peer-to-Peer (P2P) video multicast. The proposed scheme is based on a full cooperation established between the peer sites, which contribute their upload capacity during video distribution. The source site splits the video content into many small blocks and assigns each block to a single peer for redistribution. Our solution is implemented in a fully meshed P2P network in which peers are connected to each other via UDP (User Datagram Protocol) links. The video content is encoded by using the Scalable Video Coding (SVC) method. We present a flow control mechanism that allows us to optimize dynamically the overall throughput and to automatically adjust video quality for each peer. Thus, peers with different upload capacity receive different video quality. We also combine the SVC method with Multiple Description Coding (MDC) to alleviate the packet loss problem. We implemented and tested this approach in the PlanetLab infrastructure. The obtained results show that our solution achieves good performance and remarkable video quality in the presence of packet loss.

scholarly journals A Mobile Fog Computing-Assisted DASH QoE Prediction Scheme

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Hongyun Zheng ◽  
Yongxiang Zhao ◽  
Xi Lu ◽  
Rongzhen Cao
Keyword(s):  
Quality Of Experience ◽  
Fog Computing ◽  
Video Quality ◽  
Adaptive Streaming ◽  
Network Operator ◽  
Video Traffic ◽  
Client Server ◽  
Prediction Scheme ◽  
Network Equipment

Video service has become a killer application for mobile terminals. For providing such services, most of the traffic is carried by the Dynamic Adaptive Streaming over HTTP (DASH) technique. The key to improve video quality perceived by users, i.e., Quality of Experience (QoE), is to effectively characterize it by using measured data. There have been many literatures that studied this issue. Some existing solutions use probe mechanism at client/server, which, however, are not applicable to network operator. Some other solutions, which aimed to predict QoE by deep packet parsing, cannot work properly as more and more video traffic is encrypted. In this paper, we propose a fog-assisted real-time QoE prediction scheme, which can predict the QoE of DASH-supported video streaming using fog nodes. Neither client/server participations nor deep packet parsing at network equipment is needed, which makes this scheme easy to deploy. Experimental results show that this scheme can accurately detect QoE with high accuracy even when the video traffic is encrypted.

Measuring Video Quality on Full Scalability of H.264/AVC Scalable Video Coding

2008 ◽  
Vol E91-B (5) ◽  
pp. 1269-1278 ◽  
Author(s):  
C. S. KIM ◽  
S. H. JIN ◽  
D. J. SEO ◽  
Y. M. RO
Keyword(s):  
Video Coding ◽  
Scalable Video Coding ◽  
Video Quality ◽  
Scalable Video

scholarly journals Video Quality of Experience Metric for Dynamic Adaptive Streaming Services Using DASH Standard and Deep Spatial-Temporal Representation of Video

2020 ◽  
Vol 10 (5) ◽  
pp. 1793
Author(s):  
Lina Du ◽  
Li Zhuo ◽  
Jiafeng Li ◽  
Jing Zhang ◽  
Xiaoguang Li ◽  
...  
Keyword(s):  
Video Compression ◽  
Quality Of Experience ◽  
Feature Vector ◽  
Video Quality ◽  
Superior Performance ◽  
Adaptive Streaming ◽  
Input Feature ◽  
Streaming Services ◽  
Content Characteristics

DASH (Dynamic Adaptive Streaming over HTTP (HyperText Transfer Protocol)) as a universal unified multimedia streaming standard selects the appropriate video bitrate to improve the user’s Quality of Experience (QoE) according to network conditions, client status, etc. Considering that the quantitative expression of the user’s QoE is also a difficult point in itself, this paper researched the distortion caused due to video compression, network transmission and other aspects, and then proposes a video QoE metric for dynamic adaptive streaming services. Three-Dimensional Convolutional Neural Networks (3D CNN) and Long Short-Term Memory (LSTM) are used together to extract the deep spatial-temporal features to represent the content characteristics of the video. While accounting for the fluctuation in the quality of a video caused by bitrate switching on the QoE, other factors such as video content characteristics, video quality and video fluency, are combined to form the input feature vector. The ridge regression method is adopted to establish a QoE metric that enables to dynamically describe the relationship between the input feature vector and the value of the Mean Opinion Score (MOS). The experimental results on different datasets demonstrate that the prediction accuracy of the proposed method can achieve superior performance over the state-of-the-art methods, which proves the proposed QoE model can effectively guide the client’s bitrate selection in dynamic adaptive streaming media services.

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