基于缓存补偿的视频码率自适应算法

针对基于超文本传输协议（HTTP）的动态自适应流（DASH）码率自适应算法未能充分利用视频缓存以及平均码率偏低的问题,提出一种DASH标准的基于缓存补偿的码率自适应切换（BASBC）算法。首先,根据最近下载分片的下载速率分析带宽波动程度并得到预估带宽;其次,依据预估带宽和当前码率等级在缓存区设置码率上切阈值和码率下切阈值,并利用动态上切阈值控制码率向上切换,消耗缓存时长,而利用动态下切阈值控制码率向下逐级切换,累积缓存时长,从而在缓存区形成累积-消耗的缓存状态循环。BASBC算法在视频播放平均码率上高于动态自适应的HTTP流码率渐进切换（DASBS）算法,有效提高了带宽利用率;虽然所提算法的平均码率稍低于基于DASH标准的码率平滑切换（RSS）算法,但所提算法的码率切换更为平滑,整体切换稳定性表现更优。实验结果表明,所提算法在动态网络环境中具有高带宽利用、切换平滑且稳定的良好表现,能够有效提高用户的体验质量（QoE）。     

运动感知基于缓存的自适应视频流传输

目的 基于缓存的自适应视频流传输策略无需估测实时带宽,直接通过缓存变化量与码率的映射函数选取符合当前网络状况的最佳质量码流传输。传统基于缓存的自适应视频传输不考虑内容特征,在码率选择上为不同运动级别视频内容均使用相同的码率映射函数,在不稳定的无线网络环境中高运动强度内容的码率急剧降低会严重伤害用户体验质量（QoE）,提出运动感知基于缓存的自适应视频流传输（MA-BBA）算法。方法 MA-BBA算法根据片段运动级别确定码率映射函数,对运动强度高的内容快速切换到较高码率,而对于运动强度较低的内容则使用较为保守的码率,从而使得缓存资源能够位于安全边界之上且较多分配给高级别运动内容,提高不同运动强度内容的平均质量,使整体QoE得到优化。结果 在公开的无线网络带宽数据集上实现本文MA-BBA算法,基于吞吐量的自适应传输算法（TBA）和基于缓存的自适应传输算法（BBA）。MA-BBA在高运动强度内容的平均质量上比TBA和BBA分别提高1.7%和1.2%,且质量波动区间更小。MA-BBA在平均缓存利用率上达到72%,大大高于TBA的45.9%和BBA的45.4%。结论 MA-BBA算法与现有的码率自适应算法TBA和BBA相比,大大提高了缓存资源利用率,提高了对资源要求最苛刻的高级别运动内容的传输质量,减小码率切换幅度频率,优化了视频服务的整体QoE。     

基于状态机的视频码率自适应算法

由于链路带宽存在随机性,已有的基于超文本传输协议的动态自适应流媒体传输技术（DASH）的码率自适应算法不能很好解决播放流畅性和视频质量之间的矛盾。为解决该问题,提出一种基于状态机的DASH（SDASH）算法,将码率切换过程用状态机进行分析与控制。首先充分考虑客户端观看体验质量（QoE）的影响因素,对影响因素进行数值分析,并设定6个码率等级状态;然后将视频码率与影响因素的数值变化之间的联系作为状态转移条件;最后在保证播放缓存和码率偏移率处于一定阈值的条件下将视频码率切换至视频质量和播放流畅性整体性能相对最佳的码率等级上。实验结果表明,该算法与基于模糊逻辑控制的码率自适应算法相比能够提高客户端请求视频的平均码率,且尽量避免出现码率骤降等情况,从而较好地平衡播放流畅性和视频质量之间的关系,提升了视频观看过程的体验质量。     

基于网络流量预测的DASH系统优化

近年来基于超文本传输协议（HTTP）的自适应视频流量大幅上升,传统HTTP动态自适应流（DASH）速率算法无法准确预测网络吞吐量,导致网络带宽波动,使传输控制协议慢启动并触发抛弃规则,从而降低视频质量。提出一种基于网络流量预测的改进DASH速率算法。将DASH算法分为视频质量选择阶段、视频下载阶段和请求等待阶段,在视频质量选择阶段引入支持向量回归模型和长短期记忆网络预测网络吞吐量,结合缓冲时长选择更优质量的视频片段,在视频下载阶段通过预测实时吞吐量降低触发抛弃规则的次数。仿真结果表明,该算法可自适应流速率并减少抛弃规则的命中次数,有效提高视频体验质量。     

基于DASH标准的码率平滑切换算法

针对现有的基于超文本传输协议的动态自适应流（DASH）码率自适应算法在无线网络环境中码率切换频繁以及平均码率偏低的问题，提出一种基于DASH标准的码率平滑切换（RSS）算法。首先，算法的带宽检测机制利用滑动窗口对历史下载速率进行取样以计算带宽偏移系数，根据带宽偏移系数的大小初步判断带宽的波动情况，并对波动较大的情况进一步确定是否存在一致性变化趋势，从而区分出带宽的持续变化和短暂抖动，同时计算出相应情况下的带宽预测值；其次，算法的码率决策模型综合考虑带宽波动情况、缓存占用和变化情况及带宽预测值等，并且分别采取快速缓冲（FB）、缓慢切换（SS）、快速上升（FR）、约束下降（LD）、稳定保持（SH）策略和休眠机制对码率选择过程进行动态控制。实验结果表明，与基于模糊逻辑的码率自适应算法以及吞吐量调节驱动的码率自适应算法相比，该算法在播放开始时能够在最短时间内将视频码率提升到最佳水平，提高视频播放过程中的平均码率，在播放过程中带宽骤变和交替性频繁波动情况下都能够很好地维持视频码率的稳定，将切换次数降到最低，从而获得良好的无线端视频体验质量。     

MEC环境下增强ABR视频缓存传输

提出移动边缘计算环境下增强自适应比特流视频缓存传输方案。首先针对网络效用最大化问题提出基于改进的K-means算法对移动用户进行动态分组，解决码率分配子问题；然后将缓存策略和无线资源联合分配归纳为一个多对多匹配问题，同时通过等效带宽，用户可以根据时变信道选择无线链路，灵活地调整实际传输比特率版本。仿真实验结果表明，MEC-ABR方案与其他方案相比，能够同时提高缓存命中率和吞吐量。     

基于GPRS网络的流媒体自适应速率算法

在研究GPRS网络特性的基础上,设计基于RTP／RTCP的流媒体自适应速率传输算法。此算法包括首发视频选择算法,丢包率预测算法,及自适应视频码率调整算法。最后算法仿真实验结果表明首发视频选择算法可以有效减少视频传输途中的切换次数;丢包率预测算法不论在平稳或是变化剧烈的网络环境下,都能较为准确地预测下一时刻的网络丢包率;自适应视频码率调整算法可以有效地使得码率尽快的收敛。     

基于视点预测的全景视频码率自适应策略

为解决全景视频传输中存在的视频卡顿多、用户体验质量（quality of experience,QoE）低等问题,研究当前主流的视点自适应传输方案,提出一种基于视点预测的码率自适应策略（VPBAS）。首先,构建了一种基于长短期记忆网络和全卷积网络的视点预测模型,模型将视点数据和视频显著性信息进行特征融合,实现不同模态数据的相互补充和修正,提高视点预测的准确率;然后,客户端采用随机森林算法预测当前的可用带宽,并根据视点预测结果和可用带宽信息为视频分块选择码率。最后,客户端把选择的码率信息定期发送给服务器,服务器根据反馈的信息向客户端推送最佳码率的全景视频流,这种交互过程在视频播放期间不断地重复,直至客户端观看完毕。实验结果表明,与现有传输方案相比,VPBAS能有效提高带宽受限情况下的视频观看体验。     

基于DASH的混合控制码率算法

针对平滑流（SF）算法在带宽预测时存在的"毛刺"现象以及仅依靠带宽预测而没有缓存区控制所导致的频繁播放停滞的问题,提出一种动态自适应混合控制码率算法。首先,通过使用标准差来代替原SF算法中波动参数的计算,消除了预测带宽存在的"毛刺"现象;其次,针对原SF算法存在没有考虑缓存区状态所带来的频繁播放停滞问题以及传统缓存区控制算法存在分级困难的问题,引入一种基于缓存区容量的新型缓存区控制策略;最后,将改进SF算法与新型缓存区控制策略相结合形成混合算法来选择视频码率。实验结果表明,混合算法不但消除了原SF算法在带宽预测中存在的毛刺现象,而且弥补了仅依靠单一算法选择码率的不足,使得所选码率视频既降低了播放停滞频率（恶劣网络环境下效果明显,降低43%左右）又符合实际网络情况,提高了用户的观看体验。     

高铁场景中基于DASH协议的流媒体自适应云协同传输方法

高铁逐渐成为流行的出行选择, 高铁途中用户对流媒体服务有着较高的需求. 但是高速移动场景下用户带宽抖动严重、用户的媒体体验难以得到保障. 为此, 本文提出了一种基于DASH协议的跨层流媒体自适应云协同传输优化方法. 首先提出了一个基于DASH协议的跨层流媒体自适应云协同传输架构, 并提出一个高铁环境下用户的QoE模型. 在此基础上构建了基于DASH的跨层流媒体自适应云协同传输的优化模型, 并提出了基于DASH协议的跨层流媒体云协同自适应码率选取算法, 提升用户的媒体体验. 最后仿真实验结果表明, 本文提出的方法能很好地提高高铁用户的媒体体验, 有助于高速移动场景下流媒体优化传输的研究.     

Effective client-driven three-level rate adaptation (TLRA) approach for adaptive HTTP streaming

Multimedia streaming allows consumers to view multimedia content anywhere. However, quality of service is a major concern amid heightened levels of network traffic caused by increasing user demand. Accordingly, media streaming technology is adopting a new paradigm: adaptive HTTP streaming (AHS). AHS is widely used for real-time streaming content delivery in the Internet environment. In streaming, selection of appropriate bitrate is crucial for adapting media rate to network variations and client processing capabilities while ensuring optimal service for the consumer. We evaluate a proposed client-driven three-level optimized rate adaptation algorithm for adaptive HTTP media streaming. In the first stage, the algorithm chooses a suitable starting bitrate close to the available channel capacity. Next, the algorithm monitors the client parameters in real time, precisely detecting network variations and choosing a likely available bit representation for the next download segment. The algorithm controls and minimizes the effects of buffer stalls and overflow resulting from the brief network variations occurring between consecutive segments. The proposed algorithm is implemented in Dynamic Adaptive Streaming over HTTP (DASH) player and its performance compared to that of commercially available Gstreamer-based HTTP Live Streaming (HLS) and DASH players which use conventional segment fetch time–based adaptation and throughput-based adaptation algorithms respectively. This evaluation uses a real-time cloud server client and test bed streaming setup. The resulting analysis shows that the client-driven three-level rate adaptation (TLRA) approach allows adaptive streaming clients to maximize use of end-to-end network capacity, delivering an ideal user experience by precisely predicting network variations and rapidly adapting to available bandwidth, minimizing rebuffering events and bitrate level changes.     

Bitrate allocation among multiple video streams to maximize profit in content delivery networks

Concomitant with rapid advancements in video streaming technology, concurrence video traffic has increased significantly on communication channels. Conflicts often arise among the various video streams on these communication channels when the available bandwidth is limited because the bitrates and transmission range required often vary. This study proposes a server-side-based rate allocation algorithm for content delivery networks (CDNs). Instead of simply considering bitrate selection from the perspective of network and client conditions, the algorithm combines user experience with video bitrate allocation to maximize viewer engagement. First, the values of users are evaluated and a user value computation method is proposed. Second, we developed a profit maximization bitrate allocation approach (PMBAA) that enables both content providers and CDNs to maximize profits by providing guaranteed video quality. At last, the results of test bed experiments and analyses verify that PMBAA enables high-value clients to obtain more preferable bitrates than the HTTP live streaming algorithm developed by Apple Inc.     

Priority-based Media Delivery using SVC with RTP and HTTP streaming

Media delivery, especially video delivery over mobile channels may be affected by transmission bitrate variations or temporary link interruptions caused by changes in the channel conditions or the wireless interface. In this paper, we present the use of Priority-based Media Delivery (PMD) for Scalable Video Coding (SVC) to overcome link interruptions and channel bitrate reductions in mobile networks by performing a transmission scheduling algorithm that prioritizes media data according to its importance. The proposed approach comprises a priority-based media pre-buffer to overcome periods under reduced connectivity. The PMD algorithm aims to use the same transmission bitrate and overall buffer size as the traditional streaming approach, yet is more likely to overcome interruptions and reduced bitrate periods. PMD achieves longer continuous playback than the traditional approach, avoiding disruptions in the video playout and therefore improving the video playback quality. We analyze the use of SVC with PMD in the traditional RTP streaming and in the adaptive HTTP streaming context. We show benefits of using SVC in terms of received quality during interruption and re-buffering time, i.e. the time required to fill a desired pre-buffer at the receiver. We present a quality optimization approach for PMD and show results for different interruption/bitrate-reduction scenarios.     

动态自适应的HTTP流码率渐进切换算法

针对在动态网络环境和有限缓存容量限制下用户观看视频体验质量低的问题，提出一种综合考虑带宽和缓存因素的动态自适应的HTTP流码率渐进切换（DASBS）算法。首先，利用滑动窗口分析最近下载分片得到带宽初步估值；然后，依据实时带宽波动程度和缓存状态设置双矫正因子进一步平滑带宽估值；最后，设置缓存阈值使其与当前码率建立相关关系，并利用带宽估值和缓存动态阈值共同控制码率切换。使用libdash平台验证算法性能，DASBS在切换平滑性上优于VQCQ（Video Quality Control for QoE）算法，视频播放平均码率更高，有效提高了带宽利用率；平均码率稍低于EABS（Evolution of Adaptive Bitrate Switching）算法时，切换次数得到大幅下降，整体切换稳定性表现更优。实验结果表明，所提算法在动态网络环境中具有带宽高利用、切换平滑和切换稳定的良好表现，能够有效改善用户体验。     

QoE driven cross-layer scheme for DASH-based scalable video transmission over LTE

Recently using Scalable Video Coding (SVC) in Dynamic Adaptive Streaming of over HTTP (DASH) has attracted more and more attention. In this paper, we present a Quality-of-Experience (QoE) driven Cross-Layer Scheme (QCLS) for DASH-based scalable video transmission over LTE. Specifically, assuming the priority-based extraction be exploited for bitstream adaption, we first propose a new continuous Rate-Distortion (RD) model for scalable video stream. Then to guarantee continuous playback, a two-level rate adaption algorithm is presented: a novel throughput-based algorithm is implemented for dynamic selection of segment bitrate on the DASH client side at the first level, and the second level applies the rate adaption by designing a suitable packet scheduling strategy at the Base Station. The packets of each segment with lower priority that are still left in the packet queues when their playback deadline is missed, would be considered as the ones that are beyond the actual transmission ability and discarded by the second-level rate adaption. Furthermore, in order to reasonably utilize the wireless resources in LTE (Long-Term Evolution) system, a cross-layer optimization problem that maximizes the total weighted received quality of the currently transmitted segments for all clients is formulated. In view of its high complexity of obtaining the optimal solution, we develop an approach of the suboptimal solution, which can determine a locally optimal transmission strategy in resource allocation as well as the corresponding Modulation and Coding Scheme. Accordingly, the transmission rate of each client can be obtained. Simulation results show that our proposed cross-layer scheme can provide better performance than the existing ones for DASH-based scalable video transmission over LTE.     

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...