Queueing Theoretic Approach to Playout Buffer Model for HTTP Adaptive Streaming

Video is expected to be the dominant application by traffic volume over mobile networks in the near future. Mobile network operators are deploying video optimization techniques to enhance the user experience and network utilization for video delivery. Video optimization is typically “out-of-network” and includes techniques such as transcoding, transrating, time shifting and pacing that are implemented outside of mobile radio access and core networks. However, such techniques cannot easily exploit information about real time cell congestion and radio conditions for the video terminals because it is difficult to obtain such information from the radio access network. We propose a novel “in-network” video optimization technique, named Adaptive Guaranteed Bit Rate (AGBR), for HTTP-based Adaptive Streaming (HAS) video. This optimization technique is implemented at the base station and can thus exploit knowledge of the radio and congestion conditions. With only limited knowledge of the video stream properties or content, AGBR works by adjusting the throughput delivered to the different HAS clients that in turn adjust the video quality they request. The optimization algorithm maximizes aggregate quality across multiple video flows served by the base station without starving data clients, thereby improving the overall quality of experience. We demonstrate through extensive analytical modeling and simulations that AGBR can adapt to changing network conditions to support more video sessions at an acceptable quality than alternative algorithms, while enforcing fairness among all users competing for resources within a sector.

HTTP-based Adaptive Streaming (HAS) has become a widely-used video delivery technology. Use of HTTP enables relatively easy firewall/NAT traversal and content caching. While caching is an important aspect of HAS, there is not much public research on the performance impact proxies and their policies have on HAS. In this paper we build an experimental framework using open source Squid proxies and the most recent Open Source Media Framework (OSMF). A range of content-aware policies can be implemented in the proxies and tested, while the player software can be instrumented to measure performance as seen at the client. Using this framework, the paper makes three main contributions. First, we present a scenario-based performance evaluation of the latest version of the OSMF player. Second, we quantify the benefits using different proxy-assisted solutions, including basic best effort policies and more advanced content quality aware prefetching policies. Finally, we present and evaluate a cooperative framework in which clients and proxies share information to improve performance. In general, the bottleneck location and network conditions play central roles in which policy choices are most advantageous, as they significantly impact the relative performance differences between policy classes. We conclude that careful design and policy selection is important when trying to enhance HAS performance using proxy assistance.

In today's Internet, bandwidth dynamics are inevitable, and hence, the bitrate for live streaming applications should also be dynamically adjusted. However, in existing HTTP-based adaptive streaming (HAS), bitrate switching can only be performed at segment boundaries, making decisions unresponsive and often inaccurate. In this paper, we start from a close investigation on the impact of the segment length in HAS and accordingly present VHAS, an extension towards intelligent variable-length segmentation, which makes client-side decisions based on the massive amount of real-time information from the network and viewers. VHAS implements a smart trigger mechanism that balances accuracy and overhead for variable-length segmentation. We further develop an adaptive bitrate switching algorithm with data-driven I-frame prediction, which is tailored to individual viewers to minimize bitrate mismatches. We evaluate VHAS via extensive trace-driven simulations, and our results demonstrate that compared with state-of-the-art solutions, VHAS achieves 15% - 49% gains in QoE, with a noticeable bandwidth reduction of 37% - 57%.

최근 QoS/QoE 기술의 일환으로 HTTP에 기초한 적응적 스트리밍 기술이 주목받고 있다. 이에 본 논문에서는, HTTP에 기초한 동적 적응적 스트리밍 기술에 대해 알아본다. 이를 기반으로 HTTP상에서 동적 적응적 스트리밍 시스템을 설계 및 구현 하였다. 이 시스템은 MPEG2-TS 파일의 비트율별 변환, Segment 분할, MPD(Media Presentation Description)의 생성과 서버와 클라이언트 간의 동적이고 적응적인 네트워크 환경에서 MPD File의 분석을 통한 비트율별 플레이어로 구성된다. 이는 다양한 네트워크 환경에서 지속적이고, 원활한 영상의 재생을 위해 다양한 멀티미디어 분야에 활용될 것이다. Recently, QoS/QoE technology as a part of the HTTP-based adaptive streaming technology has attracted attention. In this paper, we find out HTTP-based adaptive and dynamic streaming technology. It is based on a dynamic adaptive streaming system over HTTP, which is designed and implemented. The system consist of conversion by the bit rate of MPEG2-TS files, division of segment, creation of MPD(Media Presentation Description) and bitrate's player through analysis of MPD File between servers and clients in network environments of dynamic and adaptive. This diverse network environments, continuous and smooth playback of video will be used in various multimedia fields.

HTTP Adaptive Streaming (HAS) is an adaptive bitrate streaming technique which is able to adapt to the network conditions using conventional HTTP web servers. An HAS player periodically requests pre-encoded video chunks by sending an HTTP GET message. When the downloading a video chunk is finished, the player estimates the network bandwidth by calculating the goodput and adjusts the video quality based on its estimates. However, the bandwidth estimation in application layer is pretty inaccurate due to its architectural limitation. We show that inaccurate bandwidth estimation in rate adaptation may incur serious rate oscillations which is poor quality-of-experience for users. In this paper, we propose a buffer-based rate adaptation scheme which eliminates the bandwidth estimation step in rate adaptation to provide a smooth playback of HTTP-based streaming. We evaluate the performance of the HAS player implemented in the ns-3 network simulator. Our simulation results show that the proposed scheme significantly improves the stability by replacing bandwidth estimation with buffer occupancy estimation.

Interactive branched video that allows users to select their own paths through the video, provides creative content designers with great personalization opportunities; however, such video also introduces significant new challenges for the system developer. For example, without careful prefetching and buffer management, the use of multiple alternative playback paths can easily result in playback interruptions. In this paper, we present a full implementation of an interactive branched video player using HTTP-based Adaptive Streaming (HAS) that provides seamless playback even when the users defer their branch path choices to the last possible moment. Our design includes optimized prefetching policies that we derive under a simple optimization framework, effective buffer management of prefetched data, and the use of parallel TCP connections to achieve efficient buffer workahead. Through performance evaluation under a wide range of scenarios, we show that our optimized policies can effectively prefetch data of carefully selected qualities along multiple alternative paths such as to ensure seamless playback, offering users a pleasant viewing experience without playback interruptions.

We propose a refinement of TCP Westwood which allows management of the efficiency/friendliness-to-NewReno tradeoff. We show that the refined TCP Westwood is able to achieve higher efficiency yet at the same time maintain friendliness. TCP Westwood (TCPW) implements a novel window congestion control algorithm based on available bandwidth estimation. The performance of TCPW has been promising, exceeding that of TCP NewReno in high speed and/or wired/wireless networks. However, under certain circumstances, TCP NewReno may experience some performance degradation because TCPW possesses more information and thus can take better advantage of available bandwidth. We propose combining the original TCPW sampling strategy that produces available bandwidth estimates (BE), with a new strategy that produces rate estimates (RE). Our studies show that RE works when packet losses are mostly due to congestion. If on the other hand, the packet losses are mostly due to link errors, BE gives better performance. To achieve the best of all worlds, we introduce a method we call combined rate and bandwidth estimation (CRB). A connection first infers the predominant cause of packet losses, and then uses the most appropriate estimation method. We also introduce the efficiency/friendliness tradeoff graph that provides better tradeoff visualization. In our experiments, we found that CRB provides a better compromise between efficiency and friendliness, and the means to manage such a tradeoff.

Recently, HTTP-based video streaming traffic has continued to increase. Therefore, video service providers have been using HTTP-based adaptive streaming (HAS) technology to reduce the traffic load of the HTTP server. Accordingly, many adaptive bit rate (ABR) schemes have been proposed to provide a high quality of experience (QoE) to video service clients. In this paper, we propose a new ABR scheme using an adaptive network-based fuzzy inference system (ANFIS), which is one of the neuro-fuzzy structures. The proposed scheme learns optimal fuzzy parameters by using (1) the learning ability of ANFIS and (2) the video streaming data providing high QoE to clients. Then, the bit rate of the next segment is determined according to these trained parameters. In the simulation using NS-3, we show that the proposed scheme selects the appropriate bit rate under various wireless network conditions and provides better QoE to clients than the existing schemes.

Dynamic adaptive streaming via HTTP (DASH) has been popular disseminated over the Internet especially under the circumstances of the time varying network, which it is currently the most challenging for providing smoothly video streaming via high quality. In DASH system, after completing the download of one segment, the player estimates the available network bandwidth by calculating the downloading throughput and then adapting the video bitrate level based on its estimations. However, the estimated bandwidth in the application layer is not accurate due to off-intervals appearance during the downloading process. To avoid the unfairness of bandwidth estimation by the clients, this work proposes a logarithmic approach for received network bandwidth, which includes increasing or decreasing this bandwidth logarithmically to converge the fair share bandwidth (estimated bandwidth). After obtaining the measured bandwidth, an adaptive bitrate algorithm is proposed by considering this measured bandwidth in addition to video buffer occupancy. The video buffer model is associated with three thresholds (i.e. one for initial startup and two for operating thresholds). When the video buffer's level stays between the two operating thresholds, the video bitrate will keep unchanged. Otherwise, when the buffer occupancy is too high or too low, an appropriate video bitrate is chosen to avoid buffer overflow/underflow. Simulation results show that the proposed scheme is able to converge the measured bandwidth to the fair share bandwidth very quickly. Also the proposed scheme is compared with conventional scheme, we found that our proposed scheme outperforms in achieving the best performance in terms of efficiency, stability and fairness.

HTTP adaptive streaming (HAS) has emerged as the main technology for video streaming applications. Multiple HAS video clients sharing the same wireless channel may experience different video qualities as well as different play-out buffer levels, as a result of both different video content complexities and different channel conditions. This causes unfairness in the end-user quality of experience. In this paper, we propose a quality-fair adaptive streaming solution with fair buffer (QFAS-FB) to deliver fair video quality and to achieve asymptotically fair play-out buffer levels among HAS clients competing for the same wireless resources in a long-term evolution (LTE) cell. In the QFAS-FB framework, the share of radio resources is optimized according to video content characteristics, play-out buffer levels, and channel conditions. The proposed solution is compared with the other state-of-the-art strategies, and the numerical results show that it significantly improves the quality fairness among heterogeneous HAS users, reduces the video quality variations, and improves the fairness among the user’s play-out buffers.

In this work, the authors present a novel bandwidth management solution for optimizing overall quality of experience (QoE) of multiple video streaming sessions. Instead of allocating bandwidth equally among competing flows, they propose to tailor the bandwidth allocation to both content complexity of requested video and playout buffer status of individual clients. The authors formulate the multi-client bandwidth allocation problem within the convex optimization framework, which is flexible enough to accommodate a wide variety of video quality metrics. Further, the authors present a practical architecture based on software defined networking (SDN) with two components: video quality monitoring and video quality optimization. Testbed-based experiments confirm that with quality-optimized allocation the network can support up to 75% more users at the same level of quality-of-experience (QoE) than conventional equal-rate allocations.

MPEG Dynamic Adaptive Streaming over Hypertext Transfer Protocol (MPEG DASH) is adaptive streaming standard developed to unify previously developed adaptive streaming technologies. Over the years various MPEG DASH based adaptive streaming algorithms have been developed, mainly hybrid algorithms that take into account the buffer occupancy, available bandwidth and other parameters like video segment size. Adaptive streaming algorithms are used to determine the next video representation in order to ensure the best possible quality of experience in varying network conditions. They must balance well between the initial video delay and the number and depth of representation switching, as well as avoid video stalling events. This paper compares three different algorithms: Rate Adaptation Algorithm (LIU), Segment-Aware Rate Adaptation (SARA) and Content-Aware Rate Adaptation (CARA) in five network test cases with three video sequences. Obtained results show that on average LIU has the most representation switching events regardless of the spatial (SI) and temporal information (TI) of video sequence and it has the lowest achieved bitrate values. Although CARA and SARA achieve similar results, due to the fact that SARA has four phases of deciding on the representation of the next segment, it has the least number of switching events and a larger value of switching depth. CARA achieves highest average values of achieved bitrate in cases when video sequences Elephants Dream and Big Buck Bunny with lower SI and TI are used.

Volumetric media has the potential to provide the six degrees of freedom (6DoF) required by truly immersive media. However, achieving 6DoF requires ultra-high bandwidth transmissions, which real-world wide area networks cannot provide today. Therefore, recent efforts have started to target efficient delivery of volumetric media, using a combination of compression and adaptive streaming techniques. It remains, however, unclear how the effects of such techniques on the user perceived quality can be accurately evaluated. In this paper, we present the results of an extensive objective and subjective quality of experience (QoE) evaluation of volumetric 6DoF streaming. We use PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple dynamic point cloud objects. By means of a thorough analysis, we investigate the perceived quality impact of the available bandwidth, rate adaptation algorithm, viewport prediction strategy and user's motion within the scene. We determine which of these aspects has more impact on the user's QoE, and to what extent subjective and objective assessments are aligned.

Internet video streaming is a hot topic in multimedia systems. A large variety of devices (computers, mobile phones, TVs, etc.) are connected to the Internet via wired or wireless networks and are capable of receiving and decoding HD video content. To enable new services like HD video streaming (e.g., online video rental), the Internet’s infrastructure was enhanced. But the Internet is still a best-effort network, which does not implement quality-of-service or admission control, resulting in time-varying bandwidth and packet delay, packet loss and network congestion. Because video streaming accounts for a considerable amount of the Internet’s traffic, video streaming needs additionally to be congestion-aware, to avoid a congestion collapse of the Internet. The Transmission Control Protocol (TCP) can adapt to changing network conditions and is currently the de facto standard protocol for congestion-aware and reliable data transmission in the Internet. This fact gave TCP-based video streaming a huge momentum. Consequently, this thesis investigates TCP-based adaptive video streaming for the Internet. The main goal is to provide a solution for congestion-aware video streaming, while still being able to achieve a reasonable performance in error-prone networks. To complement existing work on congestion-aware adaptive streaming, this thesis makes six contributions. (1) The baseline performance of TCP-based adaptive streaming is identified by means of an evaluation of different adaptive streaming approaches. The results represent a reference for further investigations. (2) An investigation on the influence of TCP’s behavior in presence of packet loss on the video streaming performance. (3) To overcome the shortcomings of TCP-based video streaming (single TCP connections fail to deliver a good performance in case of packet loss), a new approach to video streaming based on multiple request-response streams was introduced. The novelty of this system is that it is able to make use of multiple HTTP-based request-response streams while still providing TCP-friendliness. (4) A performance model of the HTTP-based request-response streams was developed, to estimate the influence of the system parameters and the network characteristics on the throughput performance. (5) A comprehensive evaluation of the HTTP-based request-response streams under diverse network conditions was conducted, to validate the model’s estimations. Additionally, the TCP-friendliness was evaluated, showing that request-response streaming systems can be configured to achieve TCP-friendliness. (6) A cellular network with high bandwidth fluctuations and RTTs was used to investigate the performance of the request-response streaming system in a mobile video streaming scenario. The results indicate that the streaming system can make good use of the available bandwidth, while the number of quality switches is kept low. While aggregating multiple TCP connections to improve the TCP streaming performance is quite common, usually the improvement comes at the cost of high deployment effort. By placing the streaming logic at the client, request-response streams can avoid this complexity. Additionally, this client-driven approach responds faster to changing network conditions and enables easy recovery from connection stalls or aborts, because the control loop is at the client. To improve the network efficiency and the scalability in terms of number of clients served, HTTP-based request-response streams can utilize HTTP proxies and caches.

HTTP Adaptive Streaming(HAS) has become widely deployed in multimedia services for its ability for providing the best Quality of Experience(QoE) for viewers with limited resource, especially in fluctuating wireless networkings. With its client-driven rate adaptation mechanism, HAS is able to adapt to fluctuating network condition and various terminals dynamically. The rate adaptation algorithm is crucial in HAS for its direct impact on the QoE. Most of researches focus on optimizing objective indicators, such as elevating mean bit-rate, lessening playout interruption, and avoiding quality switches, etc. However, the promotion in objective indicators doesn't indicate better experience of end users directly. In this paper, a novel QoE-Optimal Rate Adaptation(QORA) algorithm is proposed to achieve the best QoE. Firstly, a QoE model with iterative form is deduced for on-line evaluation. According to this model, the QORA is able to predict the possible QoE of each bit-rate level in candidate, then greedily choose the one with the highest QoE. The perceptual quality of HAS is optimized by the QORA directly and quantitatively. The simulation result shows that the proposed QORA is able to provide the optimal QoE compared with other rate adaptation schemes, although some of them have better performance in a single objective indicator.