一种基于IEEE 802.16j网络的带宽调度方案*

针对IEEE 802.16j网络,提出了一种上行业务的动态联合带宽资源调度方案。根据该网络系统树状拓扑结构,在基站BS和中继站RS间采用分级分布式资源调度体制,从而既保障了业务的带宽需求,又提升了上行带宽利用率,同时还提出了针对不同类型业务采用不同的调度算法,改善了调度的公平合理性。仿真结果表明,提出的动态联合调度方案相比严格优先级调度更具灵活性,能够在兼顾用户速率公平的条件下有效地保证各种服务的QoS要求,同时也可获得较高的带宽资源利用率。     

基于预测机制的云媒体网络自适应视频流选择算法

研究移动网络中,如何进一步提高云媒体服务带宽利用率的问题,提出一种基于预测机制的云媒体网络自适应视频流选择算法。该算法包含可用带宽预测模型和视频流决策模型。在带宽预测模型中,根据带宽评估结果把当前的网络状态划分为三个模式,每个模式对应不同的带宽预测方法,并且不同的模式能够相互转化。在视频流决策模型中,利用预测的可用带宽,结合可伸缩视频编码技术为用户自适应的调整视频质量。根据实验,在不同网络环境下,该方法能够有效的利用带宽,并提高媒体服务质量。     

基于联邦共识机制的多视频流带宽分配策略

针对瓶颈链路中视频带宽分配不均导致的用户QoE不公平以及带宽利用率低的问题,提出了一种基于联邦深度强化学习的分布式视频流公平调度策略。该策略能够根据客户端网络状态和视频QoE等级动态生成带宽分配权重因子,服务器端的拥塞控制算法则根据带宽分配权重因子为瓶颈链路中的每个视频流分配带宽,以保障瓶颈链路中视频流的公平传输。每个视频终端都运行一个带宽分配agent,且多个agent以联邦学习的方式周期性地训练,以便代理模型能够快速收敛。带宽分配agent通过共识机制同步联邦训练参数,实现了在异步播放请求条件下带宽分配agent模型参数的分布式聚合,并确保了agent模型参数的安全共享。实验结果表明,与最新方案相比,提出策略在QoE公平性和整体QoE效率方面分别提高了10%和7%,这表明提出策略在解决视频流带宽分配不均问题和提升用户体验方面具有潜力和有效性。     

基于队列时延检测的链路可用带宽流量调度方法

为有效提高链路带宽资源的利用率和减小数据传输时延,首先分析了链路中数据传输时延的特点,提出一种基于队列时延检测的链路可用带宽流量调度方法,该方法研究了在调度流量与可用带宽之间在各种不同情况下的数据传输时延变化趋势,保证了该方法较好的全局完整性。通过仿真实验证明,该方法有较好的流量调度能力,能有效地控制各链路的流量,改善链路时间响应特性和提高链路带宽利用率。     

WiMAX系统突发实时业务调度算法的改进

针对现有WiMAX系统MAC层没有提供不同优先级业务具体调度方案的现状,在分析传统分级调度算法的基础上,提出一种针对实时业务突发性的改进DFPQ调度算法实现方案。该方案在带宽分配过程中,通过判断实时业务突发性增多,强制将低优先级业务的一部分带宽分配给实时业务。GloMoSim网络仿真环境下的仿真结果证明,该调度策略在满足各业务QoS要求方面优于传统分级调度策略。     

实时数据组播调度动态带宽分配方案

针对多通道组播调度中数据分配不均带来的带宽浪费问题,提出3种动态带宽分配方案。根据数据项平均长度或数量改变分配优先级,对不同方案进行性能分析。实验表明,动态分配比静态分配最多可提高43%的带宽利用率,其中平均数据项长度最短优先分配最好。     

WLAN与EPON融合接入网上行带宽分配算法

为了提高带宽的利用率,提出一种WLAN与EPON融合接入网上行带宽分配算法。该算法将无线终端接入的业务分为不同的服务等级,以实现不同业务Qo S保证。首先,ONU-AP给各个无线终端STA分配带宽,采用IEEE 802.11E协议的简单调度算法给语音业务和一般数据业务分配带宽,利用视频流的平均速率估算视频业务的传输带宽。其次,光线路终端OLT给各个ONU-AP分配带宽,OLT根据语音业务速率和当前视频业务流量分别估算语音、视频业务在下一个轮询周期的带宽,并将剩余带宽在重负载终端中二次分配,最后给一般数据业务分配带宽。通过仿真实验,结果表明:与传统算法相比,该算法的网络时延和丢包率明显降低,实现了带宽资源的合理分配。     

无线上行视频流的跨层容错传输方法

如何通过资源受限的移动通信终端提升无线上行视频流的抗误性能是亟待解决的重要问题。通过不同通信层次的联合调度,提出了一种跨层容错传输方案。移动通信终端的网络层代理首先利用容错包调度为视频流的延时约束帧集合提供重要性分类,随后该终端的链路层代理利用无线链路单元的优先级调度实现选择性重传。在调度延时与传输带宽限制下,跨层容错传输能够将突发错误转移到延时约束帧集合的低优先级视频数据中,从而在突发易错传输环境中实现了无线链路单元粒度的渐进式传输和平稳退化。     

一种用于升空平台MAC层的调度架构

为满足升空平台的协议和不同业务流的属性需求,提出了一种完整的可以满足介质访问控制层服务质量和动态带宽分配的有效的调度方案,该调度方案不仅包括移动中心站对移动站的带宽分配,还包括移动站内上行业务的二级带宽分配架构。在仿真中通过对时隙发送特性、不同业务的延时特性,以及同种业务下不同算法的时延比较,验证了该调度方案的可行性。     

基于模糊逻辑的视频服务器接纳控制

提出一种新的基于模糊逻辑的VBR视频流接纳控制算法。该算法通过对调度周期的在线调整,使得系统所剩的磁盘I/O带宽和缓存空间达到一种等量的动态平衡,从而大大提高了系统资源的综合利用率。仿真结果表明,该算法下的接纳率比最优固定周期算法下的接纳率提高了约25％;同时,也比前人提出的动态调度周期算法的接纳率提高8％,并且算法的可行性也得到明显增强。     

Seamless switching of scalable video bitstreams for efficient streaming

Efficient adaptation to channel bandwidth is broadly required for effective streaming video over the Internet. To address this requirement, a novel seamless switching scheme among scalable video bitstreams is proposed in this paper. It can significantly improve the performance of video streaming over a broad range of bit rates by fully taking advantage of both the high coding efficiency of nonscalable bitstreams and the flexibility of scalable bitstreams, where small channel bandwidth fluctuations are accommodated by the scalability of a single scalable bitstream, whereas large channel bandwidth fluctuations are tolerated by flexible switching between different scalable bitstreams. Two main techniques for switching between video bitstreams are proposed. Firstly, a novel coding scheme is proposed to enable drift-free switching at any frame from the current scalable bitstream to one operated at lower rates without sending any overhead bits. Secondly, a switching-frame coding scheme is proposed to greatly reduce the number of extra bits needed for switching from the current scalable bitstream to one operated at higher rates. Compared with existing approaches, such as switching between nonscalable bitstreams and streaming with a single scalable bitstream, our experimental results clearly show that the proposed scheme brings higher efficiency and more flexibility in video streaming.     

An efficient scheduling algorithm for scalable video streaming over P2P networks

During recent years, the Internet has witnessed rapid advancement in peer-to-peer (P2P) media streaming. In these applications, an important issue has been the block scheduling problem, which deals with how each node requests the media data blocks from its neighbors. In most streaming systems, peers are likely to have heterogeneous upload/download bandwidths, leading to the fact that different peers probably perceive different streaming quality. Layered (or scalable) streaming in P2P networks has recently been proposed to address the heterogeneity of the network environment. In this paper, we propose a novel block scheduling scheme that is aimed to address the P2P layered video streaming. We define a soft priority function for each block to be requested by a node in accordance with the block’s significance for video playback. The priority function is unique in that it strikes good balance between different factors, which makes the priority of a block well represent the relative importance of the block over a wide variation of block size between different layers. The block scheduling problem is then transformed to an optimization problem that maximizes the priority sum of the delivered video blocks. We develop both centralized and distributed scheduling algorithms for the problem. Simulation of two popular scalability types has been conducted to evaluate the performance of the algorithms. The simulation results show that the proposed algorithm is effective in terms of bandwidth utilization and video quality.     

Optimized scalable cache management for video streaming system

An important research issue in video streaming is how to efficiently utilize the network resources to provide clients instant access to multiple video objects. Caching strategy and transmission scheme are the two essential points inside the video streaming framework. Recent research efforts on them are not sufficient due to their inflexible support for scalable encoded video streams and heterogeneous requests from clients. In this paper, we propose an optimized caching strategy (OCS) and a scalable transmission scheme (STS) for scalable coded video streaming. By exploring the characteristics of video streaming workload and system design objectives, OCS and STS work efficiently to minimize both network bandwidth cost and user access latency. Firstly, we analyze the caching problem for the proxy-assisted video streaming system and derive a maneuverable caching scenario. Secondly, we develop an efficient transmission scheme for scalable coded videos. Thirdly, we formulate a multi-objective optimization model with closed-form expressions to obtain the optimized caching strategy. Finally, with designed algorithms, an excellent compromise between two competing objectives (minimizing the bandwidth cost and the access latency) is achieved. We start our evaluation by studying the optimized caching strategy for a single video object. Then we apply the strategy to multiple video objects and illustrate the tradeoff between the optimization objectives. Our evaluation results show that compared with other caching strategies, the proposed optimized scalable caching strategy can achieve a significant reduction in bandwidth cost with even a small proxy cache size. Meanwhile, the best performance (in terms of bandwidth cost) is obtained together with the proposed scalable batch-patching transmission scheme.

Churn-aware optimal layer scheduling scheme for scalable video distribution in super-peer overlay networks

To model a layered video streaming system in super-peer overlay networks that faces with heterogeneity and volatility of peers, we formulate a layer scheduling problem from understanding some constraints such as layer dependency, transmission rule, and bandwidth heterogeneity. To solve this problem, we propose a new layer scheduling algorithm using a real-coded messy genetic algorithm, providing a feasible solution with low complexity in decision. We also propose a peer-utility-based promotion algorithm that selects the most qualified neighbor to guarantee the sustained quality of streaming despite high intensity of churn. Simulation results show that the proposed layer scheduling scheme can achieve the most near-optimal solutions compared to the four conventional scheduling heuristics in the average streaming ratio. It also highly outperforms those with different peer selection strategies in terms of the average bandwidth (6.9 % higher at least) and the variation of utilization (11.3 % lower at least).     

Joint bandwidth allocation,data scheduling and incentives for scalable video streaming over peer-to-peer networks

The overall performance of a peer-to-peer (P2P) scalable streaming system largely depends on the strategies employed in bandwidth allocation, data scheduling and incentives. In this paper, we develop a credit-based content-aware bandwidth auction model for scalable streaming in P2P networks. It formulates multi-overlay multi-layer bandwidth request and allocation problems as auction games. Each peer in the games acts as both auctioneer and player. Being a auctioneer, it maximizes the total revenue (credits) by selling upload bandwidth; Being a player, it uses the credits earned in bandwidth sales to sequentially bid for layer bandwidth so as to maximize the received video quality. Also, a content-aware bidding strategy is proposed, under which the required bandwidth quantity from a peer is determined by the informative video chunks and the marginal net utility that peer could provide, as well as the available credits and the maximum layer bit rate. The convergence of the proposed auction algorithm is mathematically proved. Finally, the performance of the proposed scheme is verified by simulation results.     

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.

大规模客户端视频流带宽调度

视频流服务的迅猛发展, 大规模用户共享带宽链路的场景不断增多. 现存的DASH视频流采用的ABR算法多用于提高单客户端用户的体验质量(quality of experience, QoE), 还有一些算法仅针对数个客户端的情况. 本文提出一种应用于大规模客户端场景的带宽调度算法, 通过聚类算法减小调度规模, 再将带宽分...     

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.     

Storage technique for real-time streaming of layered video

Scalable streaming technology has been proposed to effectively support heterogeneous devices with dynamically varying bandwidth. From the file system’s point of view, scalable streaming introduces another dimension of complexity in disk scheduling. Most of the existing efforts on multimedia file systems are dedicated to I/O scheduling algorithm and data placement scheme that efficiently guarantee I/O bandwidth. The important underlying assumption in these efforts is that most of the multimedia file accesses are simple playback operations and therefore are sequential. However, this workload characteristic is not valid in scalable streaming environment. In a scalable streaming environment, i.e., when only a subset of imagery is retrieved, the playback does not necessarily coincide with the sequential access on the file. The current file structure and the file system organization leaves much to be desired for supporting scalable streaming service. In this work, we propose a file system scheme, Harmonic Placement to efficiently support scalable streaming. The basic idea of Harmonic placement is to cluster the frequently accessed layers together to avoid unnecessary disk seeks. The data blocks are partitioned into two sets with respect to the layers: lower layers and upper layers. In Harmonic placement, the data blocks in the lower layers are placed with respect to their frame sequence and the data blocks in the upper layers are clustered according to the layers they belong to. We develop elaborate performance models for three different file system schemes: Progressive placement, Interleaved Placement and Harmonic Placement. We investigate the performance of the file server with different file system schemes. It was found that file system performance is very sensitive to the file organization scheme. When most of the service requests are for low-quality video (e.g., 128 Kbits/s ISDN), Progressive placement scheme supports twice as many sessions as the Interleaved placement scheme. When most of the service requests are for high-quality video (e.g., 1.5 Mbits/s MPEG-2 DVD quality), Interleaved placement can support twice as many requests as Progressive placement. In both cases, Harmonic placement scheme yields the most promising performance. Primitive version of this work has appeared on Proceedings of NOSSDAV ’06, Providence, Rhode Island, USA. This work is in part funded by KOSEF throught National Research Lab (ROA-2007-000-200114-0) and by HY-SDR center at Hanyang University.