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。     

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。     

CGC:一种移动多媒体延迟优化分发策略

近年来,随着移动互联网的普及,移动多媒体应用得到了巨大的发展.然而,由于移动通信网络结构的特殊性及运营控制策略等因素,传统互联网的多媒体分发技术在移动互联网环境下不能产生预期效果.提出一种新的移动多媒体内容分发体系架构,在普通的GGSN上增加了智能缓存,使移动用户可以就近获取多媒体服务.在此基础上,提出一种新颖的协作式GGSN智能缓存策略,GGSN智能缓存之间协同工作,在满足自身服务资源约束的条件下,最小化用户获取多媒体服务的延迟.实验结果表明:该策略不仅能有效降低多媒体服务延迟,还能很好地适应于用户的移动性.     

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）。     

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。     

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DDPG的边缘计算任务卸载和服务缓存算法

当计算任务被转移到移动边缘计算（MEC）服务器上时,通过服务缓存能够降低获取和初始化服务应用程序的实时时延和带宽成本。此外,体验质量是驱动卸载决策的关键因素,有效利用有限的计算资源能够提升用户满意度。考虑一个边缘服务器帮助移动用户执行一系列计算任务的场景,建立混合整数非线性规划问题,提出一种基于深度确定性策略梯度（DDPG）的算法来联合优化服务缓存位置、计算卸载决策和资源分配,从而提高用户对服务的体验质量,最大化用户使用计算资源所节约的成本。仿真结果表明,该算法在提高用户体验质量和节约成本方面较使用无缓存策略、随机选择策略和无缓存随机选择策略的算法性能更优。     

加强位置隐私保护的策略

为了使移动用户在充分体验基于位置的服务带来的便利的同时,其隐私也能够得到保护,提出了一种新的体系架构来加强位置掩护.通过采用缓存和周期查询处理策略来提供更强大的隐私保护,同时给出了LRU缓存块替代算法来提高缓存池响应移动对象请求的有效性.该种策略使位置掩护设施能够以时间和空间属性的方式来掩盖用户的真实位置.通过缜密的系统仿真,结果表明了该策略的正确性和有效性.     

基于传感器的移动流媒体码率控制方法

移动端视频流服务日益受到重视,智能终端码率自适应机制成为研究热点。当用户在移动端观看高清电影时需要消耗大量的网络流量,然而当用户不处于观影状态、对电影不感兴趣以及观看电影距离过远时,观看高清电影并不会给用户带来良好的体验,还会造成大量无线网络资源的浪费。文中设计了基于传感器的码率决策模型,综合考虑了用户观看位置、用户兴趣和设备状态。传感器决策模型可以优化传统的码率决策机制,因此设计了基于传感器的混合码率决策方法。实验证明,该方案在无线资源紧张的情况下能够有效节约无线网络的带宽资源。     

基于模糊控制的流媒体自适应传输算法

基于HTTP的动态自适应流媒体DASH传输协议可以使用户根据自身的终端显示能力和信道条件选择合适的视频质量,是网络视频服务技术的发展方向。如何根据网络吞吐量的变化自适应地选择视频码率,以获得最佳的用户体验质量QOE,在已有的DASH系统中还没有得到很好的解决。 提出了一种基于模糊控制的自适应传输算法,将缓存的视频余量以及用户申请的视频码率和网络吞吐量的码率失配度作为输入,将预期的缓存变化量作为输出,通过模糊逻辑实现以下控制目的：(1）将缓存稳定在一个安全的区间;(2）使传输视频的平均质量最大化;(3）避免因带宽波动所造成的视频播放中断。最后,分别在两种虚拟网络环境和两种实际网络环境下进行性能测试,实验结果表明,与已有的算法相比较,本文提出的算法可以给用户带来更好的QOE。     

Discrimination of media moments and media intervals: sticker-based watch-and-comment annotation

In this paper we discuss the problem of how to discriminate moments of interest on videos or live broadcast shows. The primary contribution is a system which allows users to personalize their programs with previously created media stickers??pieces of content that may be temporarily attached to the original video. We present the system??s architecture and implementation, which offer users operators to transparently annotate videos while watching them. We offered a soccer fan the opportunity to add stickers to the video while watching a live match: the user reported both enjoying and being comfortable using the stickers during the match??relevant results even though the experience was not fully representative.     

移动互联网下视频时延的用户体验质量研究

随着移动互联网的发展,移动视频业务的迅速爆发,良好的用户体验质量(QoE) 成为运营商挽留用户的关键因素。从用户体验角度,建立不同时延下移动视频业务的评价量化 标准。通过情景实验模拟的方法,以平均主观意见分(mean opinion score,MOS)量表建立用户 视频体验质量的实数映射关系。针对用户对视频观看时延的有效反应进行分级研究,得到以下 结果：单次和多次的初始缓冲时延和卡顿时延对用户观看移动视频的影响并建立用户体验质量 评价等级。在短视频中出现单次时延在4 s 以上,用户会出现负面情绪;在相同延迟时长下, 单次的初始缓冲时延的用户体验质量略优于卡顿时延;对于在一定播放时长的移动视频业务中 出现多次卡顿,卡顿时延短但是间隔频繁的体验质量比卡顿时延长但是间隔时间长的体验质量 要更差。     

CStream: neighborhood bandwidth aggregation for better video streaming

Despite the popularity of watching videos online, challenges still remain in video streaming in many scenarios. Limited home broadband and mobile phone 3G bandwidths mean many users stream videos at compromised quality. To provide additional bandwidth for streaming, we propose CStream, a system that aggregates bandwidth from multiple cooperating users in a neighborhood environment for better video streaming. CStream exploits the fact that wireless devices have multiple network interfaces and connects cooperating users with a wireless ad-hoc network to aggregate their unused downlink Internet bandwidth. CStream dynamically generates a streaming plan to stream a single video using multiple connections, continuously adapting to changes in the neighborhood and variations in the available bandwidth. CStream is developed and evaluated on a test bed of computers, allowing for a detailed, controlled evaluation of performance. Analysis of the results shows a linear increase in throughput over single-connection streaming and improved video quality as the number of cooperating users in a neighborhood increase.     

Collaborative Interaction for Videos on Mobile Devices Based on Sketch Gestures

With the rapid progress of the network and mobile techniques, mobile devices such as mobile phones and portable devices, have become one of the most important parts in common life.Efficient techniques for watching, navigating and sharing videos on mobile devices collaboratively are appealing in mobile environment.In this paper, we propose a novel approach supporting efficiently collaborative operations on videos with sketch gestures.Furthermore, effective collaborative annotation and navigation operations are given to interact with videos on mobile devices for facilitating users’ communication based on mobile devices’ characteristics.Gesture operation and collaborative interaction architecture are given and improved during the interactive process.Finally, a user study is conducted showing that the effectivity and collaborative accessibility of video exploration is improved.     

一种移动流媒体分层协同缓存系统

随着移动互联网的发展和用户数量的增加,网络中的音视频服务普遍采用动态缓存机制来减轻回程主干网的带宽压力和提高用户的观影体验。如何根据网络和用户需求,调整不同节点的缓存内容,以减少主干网的带宽压力,是当前缓存部署迫切需要解决的问题。基于子模函数理论,本文提出主动和被动资源分配调整方案及其算法。主动方案根据资源的流行度把视频文件部署到缓存节点上,以达到用户访问代价的最小化;被动方案根据音视频流行度的变化对节点上缓存的内容进行实时调整,以提高缓存资源的利用率和用户体验,降低主干网的带宽消耗。最小访问代价算法的复杂度与缓存空间容量大小相关,在缓存空间紧张时能快速迭代出资源的分配方案。数值仿真表明,主动和被动分配资源分配方案能有效降低远程服务器的带宽压力和提高用户体验。     

CPA-VoD: Cloud and Peer-Assisted Video on Demand System for Mobile Devices

With the rapid development of WiFi and 3G/4G, people tend to view videos on mobile devices. These devices are ubiquitous but have small memory to cache videos. As a result, in contrast to traditional computers, these devices aggravate the network pressure of content providers. Previous studies use CDN to solve this problem. But its static leasing mechanism in which the rental space cannot be dynamically adjusted makes the operational cost soar and incompatible with the dynamically video delivery. In our study, based on a thorough analysis of user behavior from Tencent Video, a popular Chinese on-line video share platform, we identify two key user behaviors. Firstly, lots of users in the same region tend to watch the same video. Secondly, the popularity distribution of videos conforms with the Pareto principle, i.e., the top 20% popular videos own 80% of all video traffic. To turn these observations into silver bullet, we propose and implement a novel cloud- and peer-assisted video on demand system (CPA-VoD). In the system, we group users in the same region as a peer swarm, and in the same peer swarm, users can provide videos to other users by sharing their cached videos. Besides, we cache the 10% most popular videos in cloud servers to further alleviate the network pressure. We choose cloud servers to cache videos because the rental space can be dynamically adjusted. According to the evaluation on a real dataset from Tencent Video, CPA-VoD alleviates the network pressure and the operation cost excellently, while only 20.9% traffic is serviced by the content provider.     

一种基于P2P的短视频分享系统

现有在线短视频分享策略通常采用C/S架构,给视频服务器带来较大的带宽压力。为此,提出一种采用点对点方式的在线短视频分享系统IShare,该系统结合用户点播偏好和视频文件之间的社会网络特性实现视频分享。IShare主要包括基于点播兴趣的节点分簇和视频数据源节点的查找2个核心技术。实验结果表明,IShare具备较好的视频数据源节点查找能力,可降低视频服务器带宽资源消耗。     

Watching a Movie Alone yet Together: Understanding Reasons for Watching Danmaku Videos

By overlaying timeline-synchronized user comments on videos, Danmaku commenting creates a unique co-viewing experience of online videos. This study aims to understand the reasons for watching or not watching Danmaku videos. From a review of the literature and a pilot study, an initial pool of motivations and hindrances to Danmaku video viewing was gathered. Then, a survey involving 248 participants to identify the underlying factor structures of motivations and hindrances was conducted. Their influences on users’ attitude and behaviors with Danmaku videos were also examined. The results showed that people viewed Danmaku videos to obtain information, entertainment, and social connectedness. Introverted young men with high openness to new experience are more likely to view Danmaku videos. Infrequent viewers refused to watch Danmaku videos mainly because of the visual clutter that resulted from Danmaku comments.     

网络带宽的受限原因分析与解决方案

随着互联网内容的丰富,尤其是视频网站的出现,给网络带宽带来了很大的挑战,在观看这些网络视频时,占用的带宽很大,如果同时观看的用户较多,就会使网络带宽受到一定的限制。在网络带宽的概念和影响因素的基础上,结合网络带宽受限的原因,针对性地提出了加大基础设施建设和引导用户上网时间等解决方案。     

Search-based composition, streaming and playback of video archive content

Locating content in existing video archives is both a time and bandwidth consuming process since users might have to download and manually watch large portions of superfluous videos. In this paper, we present two novel prototypes using an Internet based video composition and streaming system with a keyword-based search interface that collects, converts, analyses, indexes, and ranks video content. At user requests, the system can automatically sequence out portions of single videos or aggregate content from multiple videos to produce a single, personalized video stream on-the-fly.