HTTP Video Streaming Research Articles

Enhancing Crowd-Sourced Video Sharing through P2P-Assisted HTTP Video Streaming

This paper introduces a decentralized architecture designed for the sharing and distribution of user-generated video streams. The proposed system employs HTTP Live Streaming (HLS) as the delivery method for these video streams. In the architecture, a creator who captures a video stream using a smartphone camera subsequently transcodes it into a sequence of video chunks called HLS segments. These chunks are then stored in a distributed manner across the worker network, forming the core of the proposed architecture. Despite the presence of a coordinator for bootstrapping within the worker network, the selection of worker nodes for storing generated video chunks and autonomous load balancing among worker nodes are conducted in a decentralized fashion, eliminating the need for central servers. The worker network is implemented using the Golang-based IPFS (InterPlanetary File System) client, called kubo, leveraging essential IPFS functionalities such as node identification through Kademlia-DHT and message exchange using Bitswap. Beyond merely delivering stored video streams, the worker network can also amalgamate multiple streams to create a new composite stream. This bundling of multiple video streams into a unified video stream is executed on the worker nodes, making effective use of the FFmpeg library. To enhance download efficiency, parallel downloading with multiple threads is employed for retrieving the video stream from the worker network to the requester, thereby reducing download time. The result of the experiments conducted on the prototype system indicates that those concerned with the transmission time of the requested video streams compared with a server-based system using AWS exhibit a significant advantage, particularly evident in the case of low-resolution video streams, and this advantage becomes more pronounced as the stream length increases. Furthermore, it demonstrates a clear advantage in scenarios characterized by a substantial volume of viewing requests.

Moving QoE for monitoring DASH video streaming: models and a study of multiple mobile clients

Objective Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) video streaming has received considerable attention in recent years. While there are a number of objective QoE models, a limitation of the current models is that the QoE is provided after the entire video is delivered; also, the models are on a per client basis. For content service providers, QoE observed is important to monitor to understand ensemble performance during streaming such as for live events or concurrent streaming when multiple clients are streaming. For this purpose, we propose Moving QoE (MQoE, in short) models to measure QoE during periodically during video streaming for multiple simultaneous clients. Our first model MQoE_RF is a nonlinear model considering the bitrate gain and sensitivity from bitrate switching frequency. Our second model MQoE_SD is a linear model that focuses on capturing the standard deviation in the bitrate switching magnitude among segments along with the bitrate gain. We then study the effectiveness of both models in a multi-user mobile client environment, with the mobility patterns being based on traces from a train, a car, or a ferry. We implemented the study on the GENI testbed. Our study shows that our MQoE models are more accurate in capturing the QoE behavior during transmission than static QoE models. Furthermore, our MQoE_RF model captures the sensitivity due to bitrate switching frequency more effectively while MQoE_SD captures the sensitivity due to the magnitude of the bitrate switching. Either models are suitable for content service providers for monitoring video streaming based on their preference.

Performance Analysis of ACTE

HTTP adaptive streaming with chunked transfer encoding can offer low-latency streaming without sacrificing the coding efficiency. This allows media segments to be delivered while still being packaged. However, conventional schemes often make widely inaccurate bandwidth measurements due to the presence of idle periods between the chunks and hence this is causing sub-optimal adaptation decisions. To address this issue, we earlier proposed ACTE (ABR for Chunked Transfer Encoding) [6], a bandwidth prediction scheme for low-latency chunked streaming. While ACTE was a significant step forward, in this study we focus on two still remaining open areas, namely, (i) quantifying the impact of encoding parameters, including chunk and segment durations, bitrate levels, minimum interval between IDR-frames and frame rate on ACTE, and (ii) exploring the impact of video content complexity on ACTE. We thoroughly investigate these questions and report on our findings. We also discuss some additional issues that arise in the context of pursuing very low latency HTTP video streaming.

Disk Prefetching Mechanisms for Increasing HTTP Streaming Video Server Throughput

Most video streaming traffic is delivered over HTTP using standard web servers. While traditional web server workloads consist of requests that are primarily for small files that can be serviced from the file system cache, HTTP video streaming workloads often service a long tail of large infrequently requested videos. As a result, optimizing disk accesses is critical to obtaining good server throughput. In this article we explore serialized, aggressive disk prefetching, a technique that can be used to improve the throughput of HTTP streaming video web servers. We identify how serialization and aggressive prefetching affect performance, and, based on our findings, we construct and evaluate Libception, an application-level shim library that implements both techniques. By dynamically linking against Libception at runtime, applications are able to transparently obtain benefits from serialization and aggressive prefetching without needing to change their source code. In contrast to other approaches that modify applications, make kernel changes, or attempt to optimize kernel tuning, Libception provides a portable and relatively simple system in which techniques for optimizing I/O in HTTP video streaming servers can be implemented and evaluated. We empirically evaluate the efficacy of serialization and aggressive prefetching both with and without Libception, using three web servers (Apache, nginx, and the userver) running on two operating systems (FreeBSD and Linux). We find that, by using Libception, we can improve streaming throughput for all three web servers by at least a factor of 2 on FreeBSD and a factor of 2.5 on Linux. Additionally, we find that with significant tuning of Linux kernel parameters, we can achieve similar performance to Libception by globally modifying Linux’s disk prefetch behaviour. Finally, we demonstrate Libception’s ability to reduce the completion time of a microbenchmark involving two applications competing for disk resources.

QoS based Admission Control using Multipath Scheduler for IP over Satellite Networks

<p>This paper presents a novel scheduling algorithm to support quality of service (QoS) for multiservice applications over integrated satellite and terrestrial networks using admission control system with multipath selection capabilities. The algorithm exploits the multipath routing paradigm over LEO and GEO satellites constellation in order to achieve optimum end-to-end QoS of the client-server Internet architecture for HTTP web service, file transfer, video streaming and VoIP applications. The proposed multipath scheduler over the satellite networks advocates load balancing technique based on optimum time-bandwidth in order to accommodate the burst of application traffics. The method tries to balance the bandwidth load and queue length on each link over satellite in order to fulfil the optimum QoS level for each traffic type. Each connection of a traffic type will be routed over a link with the least bandwidth load and queue length at current time in order to avoid congestion state. The multipath routing scheduling decision is based on per connection granularity so that packet reordering at the receiver side could be avoided. The performance evaluation of IP over satellites has been carried out using multiple connections, different file sizes and bit-error-rate (BER) variations to measure the packet delay, loss ratio and throughput.</p>

Quality of experience estimation with layered mapping for hypertext transfer protocol video streaming over wireless networks

SummaryHypertext transfer protocol (HTTP) video streaming has occupied more than half of mobile Internet traffic today. Quality of experience (QoE) estimation for HTTP video streaming is significantly meaningful for network operators, as they can utilize valuable network resources more efficiently based on the QoE estimated. But, it is very hard to estimate QoE accurately with theoretical methods. In this paper, we adopt three buffering‐related application metrics, which have been verified to be the main factors that influence the QoE of HTTP videos, together with three fundamental quality of service (QoS) metrics in network layer, to estimate QoE. We suggest a two‐layered mapping model to capture the correlation between network QoS and application QoS and that between application QoS and QoE. Then, we exploit four regression methods, that is, linear regression, back propagation neural network, general regression neural network and support vector regression (SVR), to investigate the mapping relations of the network metrics, the application buffering‐related metrics and QoE. Experimental results demonstrate that general regression neural network and SVR perform almost equally well in fitting ability, and SVR performs best among the four methods in terms of generalization ability. Finally, we analyze the influences of different network QoS parameters on QoE based on the experimental results. Copyright © 2015 John Wiley & Sons, Ltd.

A Survey on Quality of Experience of HTTP Adaptive Streaming

Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.

Adaptation engine for a streaming service based on MPEG-DASH

HTTP Video streaming has become a strong candidate for video transmission on the Internet thanks to the abundance of web infrastructure. With the recent standardization of the new MPEG Dynamic Adaptive Streaming over HTTP (DASH), the flexibility and implantation of adaptive video systems has increased due to the fact that DASH can operate on a conventional web infrastructure. In this paper we propose an estimation and adaptation system for DASH. The proposed adaptive algorithm is based on client buffer threshold and smooth throughput measures (based on the throughput of previous segments). Before DASH, the standard of Scalable Video Coding (SVC) also arose from the idea of adaptation. Both systems (adaptive system based on SVC and the proposed system for DASH) are compared in terms of Video Quality (VQ) metrics. The results show that the proposed system reacts properly to changes in the network capacity, while maintaining a minimum level of segments in the buffer. The user-perceived quality is better than in the SVC-based adaptive system although the generated traffic is higher.

Close to Optimum?

HTTP Adaptive Streaming (HAS) is the de-facto standard for over-the-top (OTT) video streaming services. It allows to react to fluctuating network conditions on short time scales by adapting the video bit rate in order to avoid stalling of the video playback. With HAS the video content is split into small segments of a few seconds playtime each, which are available in different bit rates, i.e., quality level representations. Depending on the current conditions, the adaptation algorithm on the client side chooses the appropriate quality level and downloads the respective segment. This allows to avoid stalling, which is seen as the worst possible disturbance of HTTP video streaming, to the most possible extend. Nevertheless, the user perceived Quality of Experience (QoE) may be affected, namely by playing back lower qualities and by switching between different qualities. Therefore, adaptation algorithms are desired which maximize the user’sQoEfor the currently available network resources. Many downloading strategies have been proposed in literature, but a solid user-centric comparison of these mechanisms among each other and with the global optimum is missing. The major contributions of this work are as follows. A proper analysis of the influence of quality switches and played out representations on QoE is conducted by means of subjective user studies. The results suggest that, in order to optimize QoE, first, the quality level of the video stream has to be maximized and second, the number of quality switches should be minimized. Based on our findings, a QoEoptimization problem is formulated and the performance of our proposed algorithm is compared to other algorithms and to the QoE-optimal adaptation.

Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale

Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTP-based adaptive streaming (HAS), which brings two new features. First, by using HTTP/TCP, it leverages network-friendly TCP to achieve both firewall/NAT traversal and bandwidth sharing. Second, by pre-encoding and storing the video in a number of discrete rate levels, it introduces video bitrate adaptivity in a scalable way so that the video encoding is excluded from the closed-loop adaptation. A conventional wisdom in HAS design is that since the TCP throughput observed by a client would indicate the available network bandwidth, it could be used as a reliable reference for video bitrate selection. We argue that this is no longer true when HAS becomes a substantial fraction of the total network traffic. We show that when multiple HAS clients compete at a network bottleneck, the discrete nature of the video bitrates results in difficulty for a client to correctly perceive its fair-share bandwidth. Through analysis and test bed experiments, we demonstrate that this fundamental limitation leads to video bitrate oscillation and other undesirable behaviors that negatively impact the video viewing experience. We therefore argue that it is necessary to design at the application layer using a "probe and adapt" principle for video bitrate adaptation (where "probe" refers to trial increment of the data rate, instead of sending auxiliary piggybacking traffic), which is akin, but also orthogonal to the transport-layer TCP congestion control. We present PANDA - a client-side rate adaptation algorithm for HAS - as a practical embodiment of this principle. Our test bed results show that compared to conventional algorithms, PANDA is able to reduce the instability of video bitrate selection by over 75% without increasing the risk of buffer underrun.

A QoE-Aware Scheduler for HTTP Progressive Video in OFDMA Systems

HTTP progressive video downloading is becoming increasingly relevant in wireless and mobile networks. Therefore, improving the quality of experience (QoE) for this service is important for customer satisfaction. In this letter we propose a QoE-aware scheduling solution for the HTTP progressive video downloading in OFDMA systems. Whereas most scheduling strategies consider QoS metrics such as delay, jitter or throughput, our proposal takes into account a metric that directly affects the end-users experience. In particular, we propose to use an estimation of the amount of video data stored in the player buffer as the main criterion for resource assignment. The ultimate goal of using this criterion is the avoidance of playback interruptions. In particular, we have evaluated this scheduler in an LTE network simulator for the YouTube service, which is the most visited HTTP video streaming site. Our results show a significant improvement compared to the Multicarrier Proportional Fair scheduler, e.g. the number of videos with pauses decrease from 16.1% to 0% for 12 Mbps of cell offered load.

Performance of HTTP video streaming under different network conditions

The Internet video sharing services have been gaining importance and increasing their share in the multimedia market. In order to compete effectively and provide broadcast television with a comparable level of quality, the Internet video should fulfil stringent quality of service (QoS) constraints. However, as the Internet video is based on packet transmission, it is influenced by delays, transmission errors, data losses and bandwidth limitations which can have a devastating influence on the perceived quality of the multimedia content. There are many works which describe the impact of network impairments on the Internet video. Nevertheless, little is known about how network conditions influence the video streamed by the currently popular services such as YouTube, where video is transmitted over reliable TCP/HTTP protocols. Therefore using a network simulator, we conducted an experimental evaluation of the HTTP based video transmission analysing how the network impairments mentioned above influence the streamed video. The experiments were validated against a network emulator supplied with real network traces. As a result of this work, we can state that the buffering strategies implemented by a video player are in many cases able to mitigate unfavourable network conditions what allow to play the streamed video smoothly. The results may serve Internet Service Providers so that they could tune their network characteristics in order to match the demand from HTTP video.

Design and Implementation of a Network-Adaptive Mechanism for HTTP Video Streaming

This paper proposes a network-adaptive mechanism for HTTP-based video streaming over wireless/mobile networks. To provide adaptive video streaming over wireless/mobile networks, the proposed mechanism consists of a throughput estimation scheme in the time-variant wireless network environment and a video rate selection algorithm used to increase the streaming quality. The adaptive video streaming system with proposed modules is implemented using an open source multimedia framework and is validated over emulated wireless/mobile networks. The emulator helps to model and emulate network conditions based on data collected from actual experiments. The experiment results show that the proposed mechanism provides higher video quality than the existing system provides and a rate of video streaming almost void of freezing.

Design and Implementation of a Network-Adaptive Mechanism for HTTP Video Streaming

This paper proposes a network-adaptive mechanism for HTTP-based video streaming over wireless/mobile networks. To provide adaptive video streaming over wireless/mobile networks, the proposed mechanism consists of a throughput estimation scheme in the time-variant wireless network environment and a video rate selection algorithm used to increase the streaming quality. The adaptive video streaming system with proposed modules is implemented using an open source multimedia framework and is validated over emulated wireless/mobile networks. The emulator helps to model and emulate network conditions based on data collected from actual experiments. The experiment results show that the proposed mechanism provides higher video quality than the existing system provides and a rate of video streaming almost void of freezing.