Network Bandwidth Fluctuations Research Articles

Overall Quality Prediction for HTTP Adaptive Streaming Using LSTM Network

HTTP Adaptive Streaming has become a popular solution for multimedia delivery nowadays. However, due to network bandwidth fluctuations, video quality strongly varies during streaming. Therefore, a key challenge in HTTP Adaptive Streaming is how to evaluate the overall quality of a streaming session. In this article, a machine learning approach is proposed for overall quality prediction, where each segment in a streaming session is represented by a set of features. Two options of the feature set are investigated. In the first option, we use four features, namely segment quality, content characteristics, stalling duration, and padding. The second option consists of three features, namely bitstream-level parameters, stalling duration, and padding. The features are fed into a Long Short Term Memory (LSTM) network that is capable of exploring temporal relations between impairment events of quality variations and stalling events. The overall quality is predicted from the outputs of the LSTM network using a linear regression module. Through experimental results, it is shown that the proposed approach achieves a high prediction performance and outperforms seven existing approaches. Especially, the second option is found to be both efficient and effective. The source code of the proposed approach has been made available to the public.

A Two-Tier System for On-Demand Streaming of 360 Degree Video Over Dynamic Networks

360° video on-demand streaming is a key component of the emerging virtual reality and augmented reality applications. In such applications, sending the entire 360° video demands extremely high network bandwidth that may not be affordable by today’s networks. On the other hand, sending only the predicted user’s field of view (FoV) is not viable as it is hard to achieve perfect FoV prediction in on-demand streaming, where it is better to prefetch the video multiple seconds ahead, to absorb the network bandwidth fluctuation. This paper proposes a two-tier solution, where the base tier delivers the entire 360° span at a lower quality with a long prefetching buffer, and the enhancement tier delivers the predicted FoV at a higher quality using a short buffer. The base tier provides robustness to both network bandwidth variations and FoV prediction errors. The enhancement tier improves the video quality if it is delivered in time and FoV prediction is accurate. We study the optimal rate allocation between the two tiers and buffer provisioning for the enhancement tier to achieve the optimal trade-off between video quality and streaming robustness. We also design periodic and adaptive optimization frameworks to adapt to the bandwidth variations and FoV prediction errors in realtime. Through simulations driven by real LTE and WiGig network bandwidth traces and user FoV traces, we demonstrate that the proposed two-tier systems can achieve a high-level of quality-of-experience in the face of network bandwidth and user FoV dynamics.

Convolutional Neural Network for Intermediate View Enhancement in Multiview Streaming

Multiview video streaming continues to gain popularity due to the great viewing experience it offers, as well as its availability that has been enabled by increased network throughput and other recent technical developments. User demand for interactive multiview video streaming that provides seamless view switching upon request is also increasing. However, it is a highly challenging task to stream stable and high quality videos that allow real-time scene navigation within the bandwidth constraint. In this paper, a convolutional neural network (ConvNet)-assisted seamless multiview video streaming system is proposed to tackle the challenge. The proposed method solves the problem from two perspectives. First, a ConvNet-assisted multiview representation method is proposed, which provides flexible interactivity without compromising on multiview video compression efficiency. Second, a bit allocation mechanism guided by a navigation model is developed to provide seamless navigation and adapt to network bandwidth fluctuations at the same time. These two blocks work closely to provide an optimized viewing experience to users. They can be integrated into any existing multiview video streaming framework to enhance overall performance. Experimental results demonstrate the effectiveness of the proposed method for seamless multiview streaming.

Scalable video coding algorithm and rate-distortion optimisation based on cloud computing

In order to provide end users with better quality video, this paper presents an adaptive multipath video stream scalable video coding algorithm, which is based on the cloud computing for H264/AVC extension, with the path of diversity provided by based on the cloud computing video distribution network. The method of using scalable video coding is finally adapted to the various end users. Moreover, it adapts to network bandwidth fluctuation by observing the changes of the available bandwidth over the multiple overlay paths. And performing rate-distortion optimisation in the basis of the end-to-end distortion estimation has given a method of reduce complexity, meanwhile, maintaining the balance of quality and cloud computing security. Experimental results show that the optimisation algorithm based on the cloud computing video distribution network is more effective to estimate network congestion, reduce video packet loss rate and reducing network latency, rate-distortion optimisation performance gain outperform the current redundancy coding scheme and traditional recursive optimal per-pixel estimation based on the optimal macro-block coding mode selection, and thus more effectively ensure the quality of the video network transmission.

Spatial Multiple Description Coding for Scalable Video Streams

The need for adapting video stream delivery over heterogeneous and unreliable networks requires self-adaptive and error resilient coding. Network bandwidth fluctuations can be handled by means of a video coding scheme which adapts to the channel conditions. However, packet losses which are frequent in wireless networks can cause a mismatch during the reconstruction in the receiver end and result in an accumulation of errors which deteriorates the quality of the delivered video. A combination of multiple description coding in pixel domain and scalable video coding schemes which addresses both video adaptation and robustness to data loss is proposed in this paper. The proposed scheme combines error concealment with spatial video scalability. In order to improve the fidelity of the reconstructed to the original frames in presence of packet loss, a multilayer polyphase spatial decomposition algorithm is proposed. Classical multiple description methods interpolate the missing data which results in smoothing and artifact at object boundaries. The proposed algorithm addresses the quality degradation due to low-pass filtering effect of interpolation methods. We also comparatively analyze the trade-off between robustness to channel errors and coding efficiency.

Design of a service platform for delay-sensitive video streaming applications based on multicast p2p and scalable MDC encoding

The great diffusion of Internet accesses in the world and the enormous evolution of the Internet technologies have strongly determined dissemination and consolidation of many applications of video content distribution. However, multicast real-time video communications for interactive service applications with a very high number of users, like for example multipoint videoconferences and interactive video games, remains a challenging research topic for the presence of very strict requirements in terms of end-to-end delay and perceived quality.The target of this paper is to propose a video distribution platform that is able to provide a huge number of users with a delay constrained multicast video streaming. To this purpose, the proposed content delivery platform is based on a peer-to-peer approach that exploits multicast overlay organization of the peers to address the challenges due to bandwidth heterogeneity. To improve reliability and flexibility, video is coded using a scalable multiple description approach. Some design guidelines are provided taking into account some requirements in terms of delay, average PSNR, robustness against underlying network bandwidth fluctuations, and fairness.

A QoE-based approach for multiple description coding (MDC) streaming over video distribution network (VDN)

In the last decade, overlay multipath video streaming has appeared as a relevant solution to cope with network conditions’ changing. However, the important challenge that still needs to be faced is how to split the video into alternative sub-streams and how to define the multiple paths. In this paper, we propose an approach that generates multiple complementary descriptions of the video based on the group of pictures (GOPs). Besides, we use the quality of experience (QoE) evaluations to adjust the streaming over multiple video distribution network (VDN) paths. For evaluating the QoE, different approaches have been tested before adopting the pseudo-subjective quality assessment (PSQA) tool as the best-suited tool for our approach. Results show the automatic adaptability of the proposed method to network bandwidth fluctuation. They also show the advantages of using multiple description coding (MDC), against the traditional multi-streams splitting strategies, especially in overloaded environments.

A practical bit stream organization algorithm for robust H.264/SVC transmission

The H.264/SVC standard, due to its desired scalable functionality and coding efficiency, provides an ideal candidate for video transmission in many scenarios such as erasure networks and networks with heterogeneous clients. In order to reduce the impact of network bandwidth fluctuation, an adaptive priority ordering (APO) algorithm for H.264/SVC bit stream organization is proposed. It arranges H.264/SVC coding layers according to their rate–distortion (R–D) tradeoff within a GOP so that transmitted video quality can be preserved in the presence of dynamic bandwidth conditions. A practical path model is developed to reduce the time and space complexity of the APO algorithm. It is shown by simulation results that the APO algorithm offers better performance than the default H.264/SVC ordering method adopted by the JSVM software and the quality layer-based ordering method proposed previously in preserving smooth video quality under the same bandwidth. The proposed algorithms could be used together with the unequal error protection (UEP) method and the joint source-channel coding scheme to combat packet loss.

Adaptive data scheduling algorithm for P2P media streaming

In order to solve the problem of losing data block under the limited bandwidth in existed algorithms,a novel scheduling algorithm was proposed for P2P network,named Least-First Scheduling Algorithm(LFSA),which was simple to implement.Furthermore,it can adjust itself automatically to adapt to network heterogeneity,bandwidth fluctuation,and overlay dynamics.Experimental results show that LFSA can shorten the start delay time and achieve better performance than conventional strategies without any knowledge of the bandwidth of its partners or any special mechanism for the load balance.

A Unified Approach to Heterogeneous Video-on-Demand Broadcasting

Video-on-demand (VoD) is an essential technology for many multimedia applications. However, it remains challenging to efficiently deliver on-demand streams to a large number of receivers in a heterogeneous network environment. Although a number of VoD broadcasting protocols have been proposed for heterogeneous receivers, these protocols considers only single-bit-rate VoD services, which cannot satisfy the diverse quality-of-service requirements of heterogeneous receivers. In this paper, we propose a unified approach to heterogeneous VoD broadcasting, called unified heterogeneous broadcasting (UHB). As a unified approach, UHB is design to integrate different VoD broadcasting protocols and different scalable video coding schemes. The main merits of UHB are threefold. First, unlike conventional VoD broadcasting protocols, UHB supports multiple-bit-rate VoD services. Second, UHB is capable of adapting to network bandwidth fluctuations. Third, UHB enables each heterogeneous receiver to obtain the best visual quality by selecting the optimum system joining time. Our performance evaluation results indicate that UHB is superior to conventional heterogeneous VoD broadcasting protocols under various network conditions.

The Virtue of Patience in Low-Complexity Scheduling of Packetized Media With Feedback

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We consider streaming pre-encoded and packetized media over best-effort networks in the presence of acknowledgment feedbacks. We first review a rate-distortion (RD) optimization framework that can be employed in such scenarios. As part of the framework, a scheduling algorithm selects the data to send over the network at any given time, so as to minimize the end-to-end distortion, given an estimate of channel resources and a history of previous transmissions and received acknowledgements. In practice, a greedy scheduling strategy is often considered to limit the solution search space, and reduce the computational complexity associated to the RD optimization framework. Our work observes that popular greedy schedulers are strongly penalized by early retransmissions. Therefore, we propose a scheduling algorithm that avoids premature retransmissions, while preserving the low computational complexity aspect of the greedy paradigm. Such a scheduling strategy maintains close to optimal RD performance when adapting to network bandwidth fluctuations. Our experimental results demonstrate that the proposed patient greedy scheduler provides a reduction of up to 50% in transmission rate relative to conventional greedy approaches, and that it brings up to 2 dB of quality improvement in scheduling classical MPEG-based packet video streams. </para>