Rate-distortion optimized distributed packet scheduling of multiple video streams over shared communication resources

We consider the problem of distributed packet selection and scheduling for multiple video streams sharing a communication channel. An optimization framework is proposed, which enables the multiple senders to coordinate their packet transmission schedules, such that the average quality over all video clients is maximized. The framework relies on rate-distortion information that is used to characterize a video packet. This information consists of two quantities: the size of the packet in bits, and its importance for the reconstruction quality of the corresponding stream. A distributed streaming strategy then allows for trading off rate and distortion, not only within a single video stream, but also across different streams. Each of the senders allocates to its own video packets a share of the available bandwidth on the channel in proportion to their importance. We evaluate the performance of the distributed packet scheduling algorithm for two canonical problems in streaming media, namely adaptation to available bandwidth and adaptation to packet loss through prioritized packet retransmissions. Simulation results demonstrate that, for the difficult case of scheduling nonscalably encoded video streams, our framework is very efficient in terms of video quality, both over all streams jointly and also over the individual videos. Compared to a conventional streaming system that does not consider the relative importance of the video packets, the gains in performance range up to 6 dB for the scenario of bandwidth adaptation, and even up to 10 dB for the scenario of random packet loss adaptation.     

Video Packet Selection and Scheduling for Multipath Streaming

This paper addresses the problem of choosing the best streaming policy for distortion optimal multipath video delivery, under network bandwidth and playback delay constraints. The streaming policy consists in a joint selection of the network path and of the video packets to be transmitted, along with their sending time. A simple streaming model is introduced, which takes into account the video packet importance, and the dependencies between packets. A careful timing analysis allows to compute the quality perceived by the receiver for a constrained playback delay, as a function of the streaming policy. We derive an optimization problem based on a video abstraction model, under the assumption that the server knows, or can predict accurately the state of the network. A detailed analysis of constrained multipath streaming systems provides helpful insights to design an efficient branch and bound algorithm that finds the optimal streaming strategy. This solution allows to bound the performance of any scheduling strategy, but the complexity of the algorithm becomes rapidly intractable. We therefore propose a fast heuristic-based algorithm, built on load-balancing principles. It allows to reach close to optimal performance with a polynomial time complexity. The algorithm is then adapted to live streaming scenarios, where the server has only a partial knowledge of the packet stream, and the channel bandwidth. Extensive simulations show that the proposed algorithm only induces a negligible distortion penalty compared to the optimal strategy, even when the optimization horizon is limited, or the rate estimation is not perfect. Simulation results also demonstrate that the proposed scheduling solution performs better than common scheduling algorithms, and therefore represents a very efficient low-complexity multipath streaming algorithm, for both stored and live video services     

Adaptive topology formation for peer-to-peer video streaming

In this paper we propose an adaptive P2P video streaming framework to address the challenges due to bandwidth heterogeneity and peer churn on the Internet. This adaptive streaming framework consists of two major components, source rate adaptation and adaptive overlay topology formation, to maximize the video quality and fully utilize the overall peer upload capacity. In the source rate adaptation, the video server adapts the video source rate automatically based on the local measurement of peers’ download rates, so that the P2P network is not overloaded beyond its bandwidth capacity and peers are able to achieve smooth video playback. To combat bandwidth heterogeneity, we propose to construct a desirable link-level homogeneous overlay topology using a Markov chain Monte Carlo method, so that peers achieve an equal per-connection upload/download bandwidth. In this link-level homogeneous network, video flows do not encounter any bottlenecks along the delivery paths, and peers achieve high download rates to ensure smooth video playback. We also design a fully distributed algorithm to implement the dual mechanisms of the adaptive topology formation and the source rate maximization. To evaluate the performance of our streaming framework, we conduct both mathematical analysis and extensive simulations. The simulation results confirm our analysis and show that the proposed distributed algorithm is able to maximize the video playback quality with fast convergence.     

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

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     

Empirical evaluation of H.264/SVC streaming in resource-constrained multihomed mobile networks

Nomadic users of streamed multimedia content in mobile networks are often faced with resource-constrained network paths that suffer from low bandwidth. Streaming high-quality video in such a challenging scenario demands a set of highly adaptive schemes, which have not been sufficiently explored in particular for the emerging H.264 Scalable Video Coding (H.264/SVC) standard. In this paper, we empirically investigate the performance of streaming H.264/SVC scalable video streams to users in multihomed mobile networks containing multiple available transmission paths. Previous work has demonstrated the feasibility of aggregating bandwidth of multiple paths to deliver video streams when no single, sufficiently high bandwidth path is available. We focus on evaluating the enhanced performance of multipath bandwidth-aggregation streaming by exploiting a quality-layers based, H.264/SVC-specific packet prioritisation scheme for quality-aware multipath packet scheduling and selective packet dropping in case of bandwidth shortage even after aggregation. Additionally, we explore a base-layer rate control scheme for H.264/SVC delivery in ultra-low bandwidth environments. Through extensive experimentation on a realistic hardware-based testbed, we obtain a comprehensive and insightful understanding of the behaviour of H.264/SVC streams when transmitted across multiple paths in mobile networks. We quantify the improvements offered by the use of H.264/SVC-specific packet prioritisation schemes compared with an existing generic scalable video prioritisation scheme, and the benefits by the use of base-layer rate control in ultra-low bandwidth situations. The performance of the multipath streaming schemes is further compared with that of an ideal single high bandwidth path. We also identify the remaining challenges that must be overcome if such streaming schemes are to offer performance close to that of the ideal single high bandwidth path.     

Video streaming over the internet with optimal bandwidth resource allocation

In this paper, an adaptive framework for video streaming over the Internet is presented. The framework is a joint design of packet scheduling and rate control with optimal bandwidth resource allocation. The transmission rate is dynamically adjusted to obtain maximal utilization of the client buffer and minimal allocation of the bandwidth. Under the constraint of the transmission rate, a prioritized packet scheduling is designed to provide a better visual quality of video frames. The packet scheduling is a refined bandwidth allocation which takes into account of varying importance of the different packets in a compressed video stream. Moreover, the proposed approach is scalable with increasing multimedia flows in the distributed Internet environment. Comparisons are made with the most current streaming approaches to evaluate the performance of the framework using the H.264 video codec. The extensive simulation results show that the average Peak Signal to Noise Ratio (PSNR) increases in our proposed approach. It provides a better quality of the decoded frames, and the quality of the decoded frames changes more smoothly. The achieved video quality among different users also has a lower fluctuation, which indicates a fair sharing of network resources.

Lossless data transmission for Internet of things application over wireless multimedia sensor networks using enhanced and optimal path scheduling approach to maximizing the quality of service

The congestion of packet forwarding between a source and destination is challenging on downlink transmission in the entire file (ex. Audio and Video). Whenever file is been uploaded to the server, a user requests for file where server transmits it without knowledge of user's bandwidth, which is a major, cause of packet loss or time duration in the receiver end. To accumulate the better solution, Enhanced and Optimal Path Scheduling Approach (EOPSA) designs to find optimal path scheduling for multimedia data transmission in multimedia sensor network over cloud server using IoT devices. EOPSA studied the multisource video-on-demand streaming in multimedia sensor networks. The method introduced a heuristic distributed protocol to find optimal route for multimedia data transmissions. Efficient way to identify the bandwidth before the transmission ensures link establishment between sender and receiver. Here, the capture of bandwidth helps to check user's system capability to forward requested media data. Based on experiment evaluation, EOPSA improves 0.20 packet delivery ratio, 130 throughput, 0.20 second average delay and 14 communication overhead for 15, 25, 50, 75, and 100 nodes compared than conventional methods.     

一种自适应的视频流化前向纠错算法

网络视频应用经常会受到数据包丢失或错误以及网络带宽资源不足的干扰.相关研究表明:在多数情况下,动态变化的网络带宽和丢包率是影响视频流化质量的关键因素.因此,为了保证视频质量,可以采用前向纠错(forward error correction,简称FEC)编码来提高视频数据传输的可靠性;同时,为了适应网络状态的变化,发送端可以调节视频数据的发送速率,并在视频源数据与FEC数据之间合理分配网络传输带宽.首先通过对视频流结构的分析,在充分考虑帧之间的依赖关系和帧类型的基础上提出了一种帧的解码模型.在此基础上,建立了用于在视频源数据和FEC数据之间分配网络带宽资源的优化算法.实验表明,该模型可以有效地适应网络状态的变化,并通过优化分配网络带宽资源来使接收端获得最大的可播放帧率.     

基于区分服务的流媒体传输模型

流媒体在网络上的应用经常会受到数据包丢失或错误以及网络带宽资源不足的干扰,使得接收方播放质量受到严重影响。本文建立了一种适合流媒体传输的区分服务模型,该模型能够使高优先级数据流(实时流媒体数据流)占用更多的带宽。仿真结果表明:该策略能使流媒体数据流获得较高的吞吐量和较低的丢包率,有效提高流媒体传输的可靠性和实时性。     

基于区分服务的流媒体传输模型

流媒体在网络上的应用经常会受到数据包丢失或错误以及网络带宽资源不足的干扰,使得接收方播放质量受到严重影响。本文建立了一种适合流媒体传输的区分服务模型,该模型能够使高优先级数据流（实时流媒体数据流）占用更多的带宽。仿真结果表明：该策略能使流媒体数据流获得较高的吞吐量和较低的丢包率,有效提高流媒体传输的可靠性和实时性。     

Failure-Aware, Open-Loop, Adaptive Video Streaming With Packet-Level Optimized Redundancy

A plethora of coding and streaming mechanisms have been proposed for real-time multimedia transmission over the Internet. However, most proposed mechanisms rely only on global (e.g. based on end-to-end measurements), delayed (at least by the round-trip-time), or statistical (often based on simplistic network models) information available about the network state. Based on recently-proposed state-of-the-art open-loop video coding schemes, we propose a new integrated streaming and routing framework for robust and efficient video transmission over networks exhibiting path failures. Our approach explicitly takes into account the network dynamics, path diversity, and the modeled video distortion at the receiver side to optimize the packet redundancy and scheduling. In the derived framework, multimedia streams can be adapted dynamically at the video server based on instantaneous routing-layer information or failure-modeling statistics. The performance of our integrated application and network-layer method is simulated against equivalent approaches that are not optimized based on routing-layer feedback and distortion modeling, and the obtained gains in video quality are quantified     

Media Flow Rate Allocation in Multipath Networks

We address the problem of joint path selection and source rate allocation in order to optimize the media specific quality of service in streaming of stored video sequences on multipath networks. An optimization problem is proposed in order to minimize the end-to-end distortion, which depends on video sequence dependent parameters, and network properties. An in-depth analysis of the media distortion characteristics allows us to define a low complexity algorithm for an optimal flow rate allocation in multipath network scenarios. In particular, we show that a greedy allocation of rate along paths with increasing error probability leads to an optimal solution. We argue that a network path shall not be chosen for transmission, unless all other available paths with lower error probability have been chosen. Moreover, the chosen paths should be used at their maximum available end-to-end bandwidth. Simulation results show that the optimal flow rate allocation carefully adapts the total streaming rate and the number of chosen paths, to the end-to-end transmission error probability. In many scenarios, the optimal rate allocation provides more than 20% improvement in received video quality, compared to heuristic-based algorithms. This motivates its use in multipath networks, where it optimizes media specific quality of service, and simultaneously saves network resources at the price of a very low computational complexity.     

IQ-Paths: Predictably High Performance Data Streams Across Dynamic Network Overlays

Overlay networks are a key vehicle for delivering network and processing resources to high performance applications. For shared networks, however, to consistently deliver such resources at desired levels of performance, overlays must be managed at runtime, based on the continuous assessment and prediction of available distributed resources. Data-intensive applications, for example, must assess, predict, and judiciously use available network paths, and dynamically choose alternate or exploit concurrent paths. Otherwise, they cannot sustain the consistent levels of performance required by tasks like remote data visualization, online program steering, and remote access to high end devices. The multiplicity of data streams occurring in complex scientific workflows or in large-scale distributed collaborations exacerbate this problem, particularly when different streams have different performance requirements. This paper presents IQ-Paths, a set of techniques and their middleware realization that implement self-regulating overlay streams for data-intensive distributed applications. Self-regulation is based on (1) the dynamic and continuous assessment of the quality of each overlay path, (2) the use of online network monitoring and statistical analyses that provide probabilistic guarantees about available path bandwidth, loss rate, and RTT, and (3) self-management, via an efficient packet routing and scheduling algorithm that dynamically schedules data packets to different overlay paths in accordance with their available bandwidths. IQ-Paths offers probabilistic guarantees for application-level specifications of stream utility, based on statistical predictions of available network bandwidth. This affords applications with the ability, for instance, to send control or steering data across overlay paths that offer strong guarantees for future bandwidth vs. across less guaranteed paths. Experimental results presented in this paper use IQ-Paths to better handle the different kinds of data produced by two high performance applications and one multimedia application: (1) a data-driven interactive high performance code with user-defined utility requirements, (2) an adaptive overlay version of the popular Grid-FTP application, and (3) a MPEG-4 Fine-Grained Scalable layered video streaming.     

An efficient delay-constrained ARQ scheme for MMT packet-based real-time video streaming over IP networks

The MPEG has recently Querydeveloped a new standard, MPEG media transport (MMT), for the next-generation hybrid media delivery service over IP networks considering the emerging convergence of digital broadcast and broadband services. On account of the heterogeneous characteristics of broadcast and broadband networks, MMT provides an efficient delivery timing model to enable inter-network synchronization, measure various kinds of transmission delays and jitters caused by the transmission delay, and re-adjust the timing relationship between the MMT packets to ensure synchronized playback. By exploiting the delivery timing model, it is possible to accurately estimate the round-trip time (RTT) experienced during MMT packet transmission. Based on the measured RTT, we propose an efficient delay-constrained automatic repeat request (ARQ) scheme, which is applicable to MMT packet-based real-time video streaming service over IP networks. In the proposed ARQ scheme, the receiver buffer fullness at the time of packet loss detection is used to compute the arrival deadline, which is the maximum allowed time for completing the requesting and retransmitting of the lost MMT packet. Simulation results demonstrate that the proposed delay-constrained ARQ scheme can not only provide reliable error recovery, but it also achieves significant bandwidth savings by reducing the number of wastefully retransmitted packets that arrive at the receiver side and exceed the allowed arrival deadline.     

Content-Aware Adaptive Media Playout Controls for Wireless Video Streaming

Video streaming is one of the killer applications for cellular communications. The MPEG-4 fine-granularity scalability video coding technique can adapt to bandwidth variation and random packet errors. In this paper, to explore the impacts of cellular channel characteristics on the tolerance of buffer performance and quality of service, a novel statistical model-based adaptive media playout (AMP) is proposed by utilizing the statistical assumptions of both arrival and departure processes for a better decision on the dynamic threshold adjustment and frame-rate adjustment. Based on third-generation cellular transmission environment, simulation results will demonstrate that as compared to other AMP schemes, the proposed AMP control provides better visual quality with lower complexity.     

Cooperative Media Data Streaming with Scalable Video Coding

Live peer-to-peer (P2P) streaming has become a promising approach for broadcasting non-interactive media content from a server to a large number of interested clients. However, it still faces many challenges such as high churn rate of peer clients, uplink bandwidth constraints of participating peers, and heterogeneity of client throuput capacities. This paper presents a new P2P network called LSONet, a collaborative peer-to-peer streaming framework for scalable layer-encoded bit streams. The contributions are the combination of the advantages of both layered conding and mesh-based packet exchange. With layered coding, it overcomes overlay bandwidth limitatioins and heterogeneity of client capacities. With mesh based overlay streaming, it can better handle peer churns, as compared to tree-based solutions. For achieving these targets, this paper employs a gossip-based data-driven scheme for partnership formation, and proposes two algorithms, optimized transmission policy (OTP) and graceful degradation scheme (GDS), for multi-layers allocation. The proposed system is completely self-organizing, and in a fully distributed fashion. Extensive simulations show that LSONet achieves higher quality of service by peer-assisted streaming and layered video coding. Also, through comparison, results show that the system outperforms some previous schemes in resource utilization and is more robust and resilient for nodes departure, which demonstrate that it is well-suited for quality adaptive live streaming applications.     

基于小波EBCOT的图像IP网络传输控制策略

为了使当前“尽力而为”的网络提供视频流服务时满足QoS要求，文章提出一种基于小波EBCOT的图像IP网络传输控制策略．通过采用基于小波EBCOT的渐进可分级编码方法，对压缩后的比特流按其重要性分层打包传输，同时根据对当前网络可用带宽的估计及信道状态的判断，区分网络拥塞及不可靠传输两种不同情况进行自适应不等重丢包保护AUPLP．软件仿真表明，该文算法可大大增强小波EBCOT编码后数据的抗误码能力，在发生数据拥塞时有助于缓解网络的过负载状况，在发生不可靠传输时接收端解码图像能平均提高1．2dB的PSNR。     

基于网络编码的对等网流媒体传输模型和算法

在对等网(peer-to-peer,简称P2P)流媒体系统中,节点(用户)的输出带宽(上行带宽)容量利用率的提高能够降低服务器的带宽开销.网络编码可以实现组播的最大吞吐率,因而具有提高系统中节点输出带宽容量利用率的潜力.将随机线性网络编码应用到P2P流媒体系统中,建立了基于随机线性网络编码的P2P流媒体传输过程模型,并据此建立传输算法的优化模型,比较研究了贪婪式算法、最少者优先算法和随机算法等.优化结果表明,随机算法可以平等均匀地获取数据包,能够最充分地利用节点的输出带宽容量,降低服务提供商的运营成本.通过对优化模型解的分析对实际系统中的传输算法给出了设计指导原则.     

Efficient algorithms of video replication and placement on a cluster of streaming servers

A cost-effective approach to building up scalable video streaming servers is to couple a number of streaming servers together in a cluster so as to alleviate the inherent storage and networking constraints of streaming services. In this article, we investigate a crucial problem of video replication and placement on a distributed storage cluster of streaming servers for high quality and high availability services. We formulate it as a combinatorial optimization problem with objectives of maximizing the encoding bit rate and the number of replicas of each video and balancing the workload of the servers. The objectives are subject to the constraints of the storage capacity and the outgoing network-I/O bandwidth of the servers. Under the assumption of single fixed encoding bit rate for all video objects with different popularity values, we give an optimal replication algorithm and a bounded placement algorithm, respectively. We further present an efficient replication algorithm that utilizes the Zipf-like video popularity distributions to approximate the optimal solutions, which can reduce the complexity of the optimal replication algorithm. For video objects with scalable encoding bit rates, we propose a heuristic algorithm based on simulated annealing. We conduct a comprehensive performance evaluation of the algorithms and demonstrate their effectiveness via simulations over a synthetic workload set.     

Distributed routing in packet-switching networks by counterpropagation network

Routing is a problem of considerable importance in a packet-switching network, because it allows both optimization of the transmission speeds available and minimization of the time required to deliver information. In classical centralized routing algorithms, each packet reaches its destination along the shortest path, although some network bandwidth is lost through overheads. By contrast, distributed routing algorithms usually limit the overloading of transmission links, but they cannot guarantee optimization of the paths between source and destination nodes on account of the mainly local vision they have of the problem. The aim of the authors is to reconcile the two advantages of classical routing strategies mentioned above through the use of neural networks. The approach proposed here is one in which the routing strategy guarantees the delivery of information along almost optimal paths, but distributes calculation to the various switching nodes. The article assesses the performance of this approach in terms of both routing paths and efficiency in bandwidth use, through comparison with classical approaches.