HTTP Adaptive Streaming Research Articles

A fair server adaptation algorithm for HTTP adaptive streaming using video complexity

The increasing popularity of online video content and adaptive video streaming services, especially those based on HTTP Adaptive Streaming (HAS) highlights the need for streaming optimization solutions. From a server perspective, the main drawback of HAS is that the user selects the quality of the next video segment without taking the server constraints into account. These constraints include the number of users simultaneously being served and the server’s congestion. Here, we present the Fair Server Adaptation (FSA) algorithm, which is designed to maximize user Quality of Experience (QoE) by tackling the server’s bottleneck problem. The algorithm provides the quality representation that is closest to the user’s request, subject to the server’s constraints. Simulation results show that compared to standard Dynamic Adaptive Streaming over HTTP (DASH) server, FSA increased the number of served users and decreased both the number of rebuffering events and the average rebuffering event duration. Furthermore, the average number of unserved users decreased to almost zero and Jain’s fairness index rose. It is clear that these changes increase users’ QoE.

Clustering‐based quality selection heuristics for HTTP adaptive streaming over cache networks

SummaryHyperText Transfer Protocol (HTTP) Adaptive Streaming (HAS) has become the de facto standard video‐streaming technology. The benefits of HAS are manifold: reliable transmission of video data avoiding artifacts caused by packet loss, easy fire wall, and Network Address Translation (NAT) traversal and the seamless reuse of existing HTTP caching infrastructure. However, introducing transparent, intermediary caching nodes on the delivery path can impact the Quality of Experience (QoE) perceived by the end user. In cache‐assisted HAS, segments can be served from different origins based on the content of the caches, causing highly fluctuating throughput and Round‐Trip Time (RTT) measurements, negatively impacting the stability and optimality of the quality decisions due to incorrect throughput estimations. In this paper, we propose heuristics that are able to use information on the streaming origin and intermediary cache contents to optimize the quality selection process. Using more accurate per origin throughput measurements, buffer starvations can be avoided. Moreover, including the cache state information in the decision process can positively impact the streaming quality. Furthermore, approximation techniques based on unsupervised incremental clustering are proposed to detect the streaming origin in absence of an external information channel. Similarly, a cache probability‐based heuristic is proposed to predict the content of the expected delivery location when this information is not transferred. With perfect information, the proposed heuristics improve the QoE with 0.52 on a scale between 1 and 5, while the approximation techniques result in a performance gain between 0.04 and 0.36 for a dynamic scenario and a reduction of buffer starvations with a factor 3 to 7.

360-Degree Video Traffic Reduction Using Cloud Streaming in Mobile

Recently, commercialization of 360° video streaming service and various studies are being conducted in a mobile environment. It also makes 360° video streaming service through a mobile cloud available. The mobile cloud can easily provide characteristics of the terminal and existing features of the cloud service, such as contents and service of the application, in the mobile environment. Using this function, it is applied to 360° video streaming services in mobile environment. Unlike the conventional filming method which only shows the angle that camera operator intended, 360° videos can display the direction desired by the viewer in real time by recording a view in every direction at the same time. By displaying the real-time 3D image information to the user, the viewer can have more realistic contents and interactive experience. 360° video typically has a resolution of 4 k or more, which causes network load in mobile streaming. The adaptive HTTP streaming service currently provides 360 resolution video streams in proportion to their bandwidth. However, this method is not responsible for the quality of the video. Therefore, we propose a high-quality video streaming method with low network load in mobile environment. 360 video is divided into FoV (field of view) and non-FoV, and the image is transmitted with high quality for FoV and low quality for non-FoV. In this paper, we propose a method of FoV on the background frame (FBF) differentiated from the existing Http adaptive streaming method. It is possible to view high resolution video in a mobile environment while maintaining a regular level of video quality even in a non-viewing area.

S2VC: An SDN-based framework for maximizing QoE in SVC-based HTTP adaptive streaming

HTTP adaptive streaming (HAS) is quickly becoming the dominant video delivery technique for adaptive streaming over the Internet. Still considered as its primary challenges are determining the optimal rate adaptation and improving both the quality of experience (QoE) and QoE-fairness. Most of the proposed approaches have relied on local information to find a result. However, employing techniques that provide a comprehensive and central view of the network resources can lead to more gains in performance. By leveraging software defined networking (SDN), this paper proposes an SDN-based framework, named S2VC, to maximize QoE metrics and QoE-fairness in SVC-based HTTP adaptive streaming. The proposed framework determines both the optimal adaptation and data paths for delivering the requested video files from HTTP-media servers to DASH clients. In fact, by utilizing an SDN controller and its complete view of the network, we introduce an SVC flow optimizer (SFO) application module to determine the optimal solution in a centralized and time slot fashion. In the current approach, we first formulate the problem as a mixed integer linear programming (MILP) optimization model. The MILP is designed in such a way that it applies defined policies, e.g. setting priorities for clients in obtaining video quality. Secondly, we show that this problem is NP-complete and propose an LP-relaxation model to enable S2VC framework for performing rate adaptation on a large-scale network. Finally, we conduct experiments by emulating the proposed framework in Mininet, with the usage of Floodlight as the SDN controller. In terms of improving QoE-fairness and QoE metrics, the effectiveness of the proposed framework is validated by a comparison with different approaches.

HTTP/2 Push-Based Low-Delay Live Streaming Over Mobile Networks With Stream Termination

HTTP adaptive streaming (HAS) has become popular for delivering videos over the Internet. However, most HAS services are currently based on the traditional HTTP/1.1, which has limitations for live streaming. Meanwhile, the new HTTP/2 was standardized in 2015 with many improvements. In this letter, we present a novel HTTP/2-based adaptation scheme for low-delay live streaming over mobile networks. The proposed scheme leverages two HTTP/2 features called server push and stream termination to enable a shorter camera-to-display delay. Experimental results with small buffer sizes show that our scheme can provide users with high video bitrate and stable buffer level. Furthermore, the buffer size in live streaming can be reduced to two sec without sacrificing video quality.

Video Quality Representation Classification of Encrypted HTTP Adaptive Video Streaming

The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new machine learning method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. The crawler codes and the datasets are provided in [43,44,51]. An extensive empirical evaluation shows that our method is able to independently classify every video segment into one of the quality representation layers with 97% accuracy if the browser is Safari with a Flash Player and 77% accuracy if the browser is Chrome, Explorer, Firefox or Safari with an HTML5 player.

Queueing Theoretic Approach to Playout Buffer Model for HTTP Adaptive Streaming

HTTP-based adaptive streaming (HAS) has recently been widely deployed on the Internet. In the HAS system, a video content is encoded at multiple bitrates and the encoded video content is segmented into small parts of fixed durations. The HAS client requests a video segment and stores it in the playout buffer. The rate adaptation algorithm employed in HAS clients dynamically determines the video bitrate depending on the time-varying bandwidth. Many studies have shown that an efficient rate adaptation algorithm is critical to ensuring quality-of-experience in HAS systems. However, existing algorithms have problems estimating the network bandwidth because bandwidth estimation is performed on the client-side application stack. Without the help of transport layer protocols, it is difficult to achieve accurate bandwidth estimation due to the inherent segment-based transmission of the HAS. In this paper, we propose an alternative approach that utilizes the playout buffer occupancy rather than using bandwidth estimates obtained from the application layer. We start with a queueing analysis of the playout buffer. Then, we present a buffer-aware rate adaptation algorithm that is solely based on the mean buffer occupancy. Our simulation results show that compared to conventional algorithms, the proposed algorithm achieves very smooth video quality while delivering a similar average video bitrate.

ORL-SDN

In designing an HTTP adaptive streaming (HAS) system, the bitrate adaptation scheme in the player is a key component to ensure a good quality of experience (QoE) for viewers. We propose a new online reinforcement learning optimization framework, called ORL-SDN, targeting HAS players running in a software-defined networking (SDN) environment. We leverage SDN to facilitate the orchestration of the adaptation schemes for a set of HAS players. To reach a good level of QoE fairness in a large population of players, we cluster them based on a perceptual quality index. We formulate the adaptation process as a Partially Observable Markov Decision Process and solve the per-cluster optimization problem using an online Q-learning technique that leverages model predictive control and parallelism via aggregation to avoid a per-cluster suboptimal selection and to accelerate the convergence to an optimum. This framework achieves maximum long-term revenue by selecting the optimal representation for each cluster under time-varying network conditions. The results show that ORL-SDN delivers substantial improvements in viewer QoE, presentation quality stability, fairness, and bandwidth utilization over well-known adaptation schemes.

DASH-Based Multi-View Video Streaming System

With the rapid development of electronic and computing technology, multi-view video transmission has attracted significant attention from the research and industrial communities. A multi-view video service provides a user with various viewpoints according to the user’s input. Alongside the recent trend of shifting video streaming service platforms to HTTP adaptive streaming, dynamic adaptive streaming over HTTP (DASH)-based multi-view video services has been widely studied. However, DASH-based multi-view video streaming suffers from high view-switching delay. The delay between a request for view switching and the rendering of the target view adversely affects the quality of experience. This paper provides an analysis of the view-switching delay of DASH-based multi-view video streaming. Based on the analysis, a novel DASH-based multi-view video streaming system is proposed. It can minimize the view-switching delay by employing a buffer control, parallel streaming, and server push schemes. The results obtained from the implementation of this solution show that the proposed system significantly reduces the view-switching delay while guaranteeing the quality of multi-view video.

HTTP 적응적 스트리밍 서비스의 QoE 향상을 위한 다중 경로 기반 전송률 조절 기법

최근 HTTP 적응적 스트리밍 서비스에서 다중 경로를 사용하여 QoE를 향상시키는 기법에 대한 많은 연구가 진행되고 있다. 하지만 다중 경로의 대역폭을 집합하면 대역폭 변동의 편차가 커져서 단일 경로를 사용할 때보다 빈번한 품질 변경이 발생하고 다중 경로를 통해 수신하는 세그먼트의 다운로드 시간이 달라 세그먼트를 재정렬 하는 시간이 추가로 필요하다. 본 논문에서는 기존 다중 경로 기반 HTTP 적응적 스트리밍 서비스의 문제점을 완화하여 QoE를 향상시키기 위한 다중 경로 기반 전송률 조절 기법을 제안한다. 제안하는 기법은 세그먼트의 재정렬 문제를 완화하기 위해 부분 세그먼트 (Partial Segment) 기반 스케줄링을 수행하며 빈번한 품질 변경을 방지하기 위해 각 경로의 대역폭 차이를 고려하여 예측된 대역폭을 평활화한다. 실험을 통해 제안하는 기법이 평균 비디오 품질을 높이고 품질 변경 빈도를 줄여 QoE를 향상시키는 것을 확인하였다.

QoE-Aware Bandwidth Broker for HTTP Adaptive Streaming Flows in an SDN-Enabled HFC Network

This paper proposes a software defined networking based bandwidth broker solution for improving viewer experience for any type of content delivered to any type of consumer device using HTTP adaptive streaming (HAS) in a hybrid fiber coax network. This solution is designed to meet per-session and per-group quality-of-experience objectives, to avoid common HAS culprits such as video instability, unfair and unequal quality distribution and network resource underutilization, and to scale to a large number of concurrent HAS sessions without introducing too much overhead. The mathematical framework behind our solution solves a convex optimization problem, which relies on a concave network utility maximization function. Results confirm the effectiveness of the proposed solution over the state-of-the-art bitrate adaptation and bandwidth allocation schemes.

Quality of Experience Driven Rate Adaptation for Adaptive HTTP Streaming

A quality of experience (QoE) driven rate adaptation approach is proposed in this paper for adaptive HTTP streaming which jointly considers both bandwidth savings and video quality adaptation into account for the rate adjustment, beneficial to both service providers and subscribers. At each decision epoch, the adaptation algorithm accumulates for the time varying QoE of a viewer by accounting for all the impairments, namely, initial delay, quality transitions, and playback interruptions, which commonly occur during the playback of a video in adaptive streaming. Based on the possible bandwidth savings and resulting QoE variations, the algorithm decides on to adapt the bitrate dynamically and accordingly maximizes viewers’ QoE. Since the proposed adaptation approach constructs an optimal path across the segments for adaptation, it also achieves QoE fairness among multiple clients in shared bandwidth environments. The experimental evaluation carried over real-world wireless network environments demonstrate that the proposed adaptation approach can maximize viewers’ QoE in adaptive streaming, especially, under highly variable throughput conditions.

Multipath-based transmission scheme for improving the QoE of HTTP adaptive streaming

The number of users using multimedia streaming services in various environments is increasing. Accordingly, HyperText Transfer Protocol (HTTP) adaptive streaming has attracted attention. Recently, there have been many studies on schemes for improving the Quality of Experience (QoE) by using multipaths in HTTP adaptive streaming. Downloading segments at the same time using multipaths can take up a high amount of bandwidth and make the transmission robust to data loss. However, when the bandwidths of the multipaths are aggregated, the variation of the bandwidth fluctuation increases, so quality changes occur more frequently. Additionally, it takes time to reorder the segments due to different segment download times received through the multipath. To this end, we propose a multipath-based transmission scheme to improve the QoE of HTTP adaptive streaming in the multipath environment. The proposed scheme improves the QoE by increasing the average video quality and reducing the frequency of video quality changes.

Quality of Experience-Centric Management of Adaptive Video Streaming Services

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming (HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users’ QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users’ QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users’ QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server- and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years.

PMS

Single-source HTTP adaptive streaming solutions (HAS) have become the de facto solutions to deliver live video over the Internet. By avoiding video stalling events that are mainly caused by the lack of throughput at client or at server side, HAS solutions increase the end users’ quality of experience (QoE). We propose to pragmatically extend HAS with our MS-Stream solution that simultaneously utilizes several servers. MS-Stream aims at offering high QoE for live content delivery by exploiting expanded bandwidth and link diversity in distributed heterogeneous infrastructures. By leveraging end users’ connectivity capacities, we further extend the QoE and scalability capabilities of our proposal by exposing a hybrid P2P/multisource live-streaming solution (P2P/MS-Stream (PMS)), achieving trade-offs between the system’s scale and the end users’ QoE. We propose a distributed quality adaptation algorithm run by every peer, along with a local optimization method of the usage of the server infrastructure made available. Large-scale evaluations conducted with 300 peers located in France permits validating our approach and algorithms over flash crowd events and allow us to conclude that PMS can reach the optimal trade-offs between QoE and system scale.

Deadline‐aware TCP congestion control for HTTP adaptive streaming services

SummaryOver the last decades, video streaming services have gained a lot of popularity, representing over 70% of the total consumer Internet traffic worldwide. While originally dedicated streaming protocols have been used to deliver these services, for several years, the video streaming industry is steadily shifting to HTTP adaptive streaming (HAS)–based delivery over TCP. With each video segment, implicit delivery deadlines are associated in order to avoid playout interruptions. However, TCP is known to be far from minimizing the number of deadline‐missing streams. In this paper, 2 deadline‐aware congestion control mechanisms are proposed, based on a parametrization of the traditional TCP New Reno congestion control strategy. By introducing deadline awareness at the transport layer, the modulation of the congestion window size is dynamically adapted to steer the aggressiveness of the considered stream to optimize the delivery of video streaming services. The proposed approaches are thoroughly evaluated in an HAS video‐on‐demand scenario over tree‐based delivery networks. It was shown that in a realistic scenario, the proposed approaches can outperform traditional congestion control strategies by up to 12% in terms of estimated mean opinion score, mainly by reducing the average video freezing time by more than 85%.

Performance evaluation of TcpHas: TCP for HTTP adaptive streaming

HTTP adaptive streaming (HAS) is a widely used video streaming technology that suffers from a degradation of user’s Quality of Experience (QoE) and network’s Quality of Service (QoS) when many HAS players are sharing the same bottleneck link and competing for bandwidth. The two major factors of this degradation are: the large OFF period of HAS, which causes false bandwidth estimations, and the TCP congestion control, which is not suitable for HAS given that it does not consider the different video encoding bitrates of HAS. This paper proposes a HAS-based TCP congestion control, TcpHas, that minimizes the impact of the two aforementioned issues. It does this using traffic shaping on the server. Simulations indicate that TcpHas improves both QoE, mainly by reducing instability and convergence speed, and QoS, mainly by reducing queuing delay and packet drop rate.

Bitrate adaptation for seamless on-demand video streaming over mobile networks

HTTP Adaptive Streaming (HAS) has become a key technology for delivering videos over the mobile Internet. To cope with throughput variations during a streaming session, an HTTP streaming client should answer two key questions: (1) “should the current bitrate be maintained?” and (2) “if not, which new bitrate should be requested?” This paper presents a buffer-based adaptation method that systematically answers these two questions. By considering the trend of the buffer level in the near future, our method reasonably decides a buffer range for maintaining the bitrate. Moreover, when the bitrate should be reduced, the proposed method smartly selects the bitrate to yield smooth reductions while avoiding buffer underflows. Through evaluation with time-varying bandwidths of mobile networks, we show that the proposed method can provide high average bitrate, gradual transitions, and buffer stability.

Adaptive video streaming testbed design for performance study and assessment of QoE

SummaryHypertext Transfer Protocol adaptive streaming switches between different video qualities, adapting to the network conditions, and avoids stalling streamed frames over high‐oscillation client's throughput improving the users' quality of experience (QoE). Quality of experience has become the most important parameter to lead the service providers to know about the end‐user feedback. Implementing Hypertext Transfer Protocol adaptive streaming applications to find out QoE in real‐life scenarios of vast networks becomes more challenging and complex task regarding to cost, agile, time, and decisions. In this paper, a virtualized network testbed to virtualize various machines to support implementing experiments of adaptive video streaming has been developed. Within the test study, the metrics which demonstrate performance of QoE are investigated, respectively, including initial delay (ie, startup delay at the beginning of playback a video), frequency switches (ie, number of times the quality is changed), accumulative video time (ie, number and length of stalls), CPU usage, and battery energy consumption. Furthermore, the relation between effective parameters of QoS on the aforementioned metrics for different segment length is investigated. Experimental results show that the proposed virtualized system is agile, easy to install and use, and costs less than real testbeds. Moreover, the subjective and objective performance studies of QoE evaluation in the system have proven that the segment lengths of 6 to 8 seconds were faired and more efficient than others according to the investigated parameters.

QoE-SDN APP: A Rate-guided QoE-aware SDN-APP for HTTP Adaptive Video Streaming

While video streaming has dominated the Internet traffic, video service providers (VSPs) compete on how to assure the best quality of experience (QoE) to their customers. HTTP Adaptive Streaming (HAS) has become the de facto way that helps VSPs work-around potential network bottlenecks that inevitably cause stallings. However, HAS-alone cannot guarantee a seamless viewing experience, since this highly relies on the mobile network operators’ (MNOs) infrastructure and evolving network conditions. Software-defined networking (SDN) has brought new perspectives to this traditional paradigm where VSPs and MNOs are isolated, allowing the latter to open their network for more flexible, service-oriented programmability. This paper takes advantage of recent standardization trends in SDN and proposes a programmable QoE-SDN App, enabling network exposure feedback from MNOs to VSPs towards network-aware video segment selection and caching, in the context of HAS. The video selection problem is formulated using Knapsack optimization and relaxed to partial sub-problems that provide segment encodings that can mitigate stallings. Furthermore, a mobility prediction mechanism based on the Self-similar Least-Action Walk model is introduced, toward proactive segment caching. A number of use cases, enabled by the QoE-SDN App, are designed to evaluate the proposed scheme, revealing QoE benefits for VSPs and bandwidth savings for MNOs.