Video traffic analytics for large scale surveillance

The video traffic analysis is the most important issue for large scale surveillance. In the large scale surveillance system, huge amount of live digital video data is submitted to the storage servers through the number of externally connected scalable components. The system also contains huge amount of popular and unpopular old videos in the archived storage servers. The video data is delivered to the viewers, partly or completely on demand through a compact system. In real time, huge amount of video data is imported to the viewer’s node for various analysis purposes. The viewers use a number of interactive operations during the real time tracking suspect. The compact video on demand system is used in peer to peer mesh type hybrid architecture. The chunk of video objects move fast through the real time generated compact topological space. Video traffic analytics is required to transfer compressed multimedia data efficiently. In this work, we present a dynamically developed topological space, using mixed strategy by game approach to move the video traffic faster. The simulation results are well addressed in real life scenario.     

Analysis and design of peer‐assisted video‐on‐demand services

We propose a deterministic fluid model to understand the trade‐offs in the design of peer‐assisted video‐on‐demand (VoD) services. There are three entities in this model: peers (or end users), seeders (altruistic users that own one or several complete video items), and cache servers that store and forward videos with a limited capacity. Peers join the network, download one or multiple concurrent video streams (possibly different video items), and abort the system when they wish. Peers are assumed to cooperate in a BitTorrent‐based fashion, governed by tit‐for‐tat and fair availability. The issue is to minimize the expected downloading times, choosing the set of video items that should be stored in each cache server. We first prove that the deterministic model is globally stable, and find closed expressions for the expected waiting times. Then, we introduce a combinatorial optimization problem (COP), whose nature is similar to the multiknapsack problem (where the items are videos, and knapsacks are related with the storage of cache servers). The problem turns out to be NP‐complete. Therefore, we heuristically address the problem following a GRASP (greedy randomized adaptive search procedure) methodology. Finally, we simulate the new caching methodology with real‐life traces taken from YouTube logs. The results suggest that the peer‐to‐peer philosophy is both stable and cost‐effective for on‐demand streaming purposes.     

Peer Assisted Video Streaming With Supply-Demand-Based Cache Optimization

In this paper, we consider a hybrid P2P video on-demand architecture that utilizes both the server and the peer resources for efficient transmission of popular videos. In our system architecture, each peer dedicates some cache space to store a particular segment of a video file as well as some of its upload bandwidth to serve the cached segment to other peers. Peers join the system and issue a streaming request to a control server. Control server directs the peers to streaming servers or to other peers who have the desired video segments. Control server also decides which peer should cache which video segment. Our main contribution in this paper is to determine the proper caching strategies at peers such that we minimize the average load on the streaming servers.      

Cost-effective capacity migration of Peer-to-Peer social media to clouds

Social media streaming has become one of the most popular applications over the Internet. We have witnessed the successful deployment of commercial systems with CDN (Content Delivery Network)- based engines, but they suffer from excessive costs for deploying dedicated servers. And with the further expansions on network traffic of social media streaming, a cost-effective solution remains an illusive goal. The emergence of cloud computing sets out to meet the challenge by dynamically leasing cloud servers. This paper aims to realize the capacity migration of social media systems to clouds at the reduced cost. Firstly, by lowering the capacity requested from clouds to reduce the capacity migration cost. Based on the crawled data from YouTube which is the most representative online social media, we find that with larger than 90% probability, the YouTube user’s all requested videos are within three hops of related videos. Then the three hops of related videos are regarded as a cluster and a user’s request can be partly satisfied by other users who watch videos in the same cluster to lessen the capacity requested from clouds. Therefore the capacity migration for clusters is under the P2P (Peer-to-Peer) paradigm and a cloud-assisted P2P social media system is proposed. Secondly, given the diverse capacities, cost, limited lease size of cloud servers, we formulate an optimization problem about how to lease cloud servers to minimize the leasing cost and a heuristic solution is presented. The evaluation based on the crawled data from a cluster of YouTube videos shows the efficiency of the proposed schemes.     

移动互联网视频传输优化的架构研究

随着3G移动互联网的快速发展,在手机等移动终端上看视频成为一种日常应用.但互联网上的大部分视频对于移动用户而言,其码率相对较大,而且移动用户的可用带宽受环境影响变化大,不稳定,影响了用户观看视频的体验.提出一种针对移动互联网的视频传输优化解决方案的系统架构,该架构从缓存和压缩两个方面入手,通过缓存缓解运营商的骨干网流量压力,通过视频压缩降低视频码率以满足用户的实际接入带宽.该架构既为运营商节约了带宽、降低了运营成本,同时也保证用户观看视频的连续性,提高用户体验.     

Parallel video streaming optimizing network throughput

In the Internet, video streaming services, in which users can enjoy videos at home, are becoming popular. Video streaming with high definition TV (HDTV) or ultra high definition video (UHDV) quality will be also provided and widely demanded in the future. However, the transmission bit-rate of high-quality video streaming is quite large, so generated traffic flows will cause link congestion. In the Internet, routes that packets take are determined using static link weights, so the network productivity, i.e., the maximum achievable throughout by the network, is determined by the capacity of a bottleneck link with the maximum utilization, although utilizations of many links remain low level. Therefore, when providing streaming services of rich content, i.e., videos with HDTV or UHDV quality, it is important to flatten the link utilization, i.e., reduce the maximum link utilization. We propose that ISPs use multiple servers to deliver rich content to balance the link utilization and propose server allocation and server selection methods for parallel delivery. We evaluate the effect of parallel delivery using 23 actual commercial ISP networks.     

基于非协作博弈的边缘分布式缓存方案

随着多媒体数据流量的急剧增长,传统云计算模式难以满足用户对于低延时和高带宽的需求.虽然边缘计算中基站等边缘设备拥有的计算能力以及基站与用户之间的短距离通信能够使用户获得更高的服务质量,但是如何利用边缘节点的收益和成本之间的关系设计边缘缓存策略,仍然是一个具有挑战性的问题.利用5G和协作边缘计算技术,在大量短视频应用场景下,提出了一种协作边缘缓存技术来同时解决以下3个问题：（1）通过减少传输延时,提高了用户的服务体验;（2）通过近距离传输,降低了骨干网络的数据传输压力;（3）分布式的工作模式减少了云服务器的工作负载.首先定义了一个协作边缘缓存模型,其中,边缘节点配备有容量有限的存储空间,移动用户可以接入这些边缘节点,一个边缘节点可以服务多个用户;其次,设计了一个非协作博弈模型来研究边缘节点之间的协作行为,每一个边缘节点看成一个玩家并且可以做出缓存初始和缓存重放策略;最后,找到了该博弈的纳什均衡,并设计了一个分布式的算法以达到均衡.实验仿真结果表明,提出的边缘缓存策略能够降低用户20%的延时,并且减少了80%的骨干网络的流量.     

A dynamic caching algorithm based on internal popularity distribution of streaming media

Most proxy caches for streaming videos do not cache the entire video but only a portion of it. This is partly due to the large size of video objects. Another reason is that the popularity of different parts of a video can be different, e.g., the prefix is generally more popular. Therefore, the development of efficient cache mechanisms requires an understanding of the internal popularity characteristics of streaming videos. This paper has two major contributions. Firstly, we analyze two 6-month long traces of RTSP video requests recorded at different streaming video servers of an entertainment video-on-demand provider, and show that the traces provide evidence that the internal popularity of the majority of the most popular videos obeys a k-transformed Zipf-like distribution. Secondly, we propose a caching algorithm which exploits this empirical internal popularity distribution. We find that this algorithm has similar performance compared with fine-grained caching but requires significantly less state information.     

一个基于 ownCloud 的 IHEPBox 个人云平台

随着当前云计算技术的快速增长,用户获得大量存储空间的免费网盘已经是很普遍的现象。为了能给用户提供一个安全的个人文档网络存储空间,本文提出以开源软件 ownCloud 构建高能所个人云平台 (IHEPBox) 的解决方案,IHEPBox 作为典型三层负载均衡的 Web 应用,管理着平台的各个方面,从用户管理到插件、文件共享到存储。后台数据库中存储用户信息、用户共享文件信息、插件应用状态和 IHEPBox 用来加速文件访问的缓存。其中用户被配置为动态地使用存储空间,基于用户目录结构,开启了基本的数据隔离和多租户模式。同时 IHEPBox 内置了一个存储抽象层,可以利用 CIFS、NFS、GFS2 和 Gluster 等类型,以挂接到服务器上的存储空间。平台集成了 ldap 数据库 (AD 域),用 WebDAV 协议将私有云存储服务器和客户端相连,实现了数据文件的实时同步。     

MSC: a multi-version shared caching for multi-bitrate VoD services

Recently, many Video-on-Demand (VoD) service providers try to attract as many users as possible by offering multi-bitrate video streaming services with differentiated qualities. Many researches focus on video layered coding (e.g., scalable video coding, SVC). However, SVC is not widely used in VoD industry. Another solution, multi-version videos, can be classified into online transcoding and pre-stored multi-version videos. Online transcoding is a CPU-intensive and costly task, so it is not suitable for large-scale VoD applications. In this paper, we study how to improve caching efficiency based on pre-stored multi-version videos. We leverage the sharing probability among different versions of the same video and propose a multi-version shared caching (MSC) method to maximize the benefit of caching proxy. If the desired version is not in the cache while the higher neighbor version is in, MSC transmits the higher version streaming to user temporarily. In this case, MSC can make full use of the caching resources to improve the cache hit ratio and decrease users’ average waiting time. Simulation results show that MSC outperforms the others in the cache hit ratio and the average waiting time.     

Content-adaptive wireless streaming of instructional videos

Video streaming over wireless networks is becoming increasingly important for a variety of applications. To accommodate the dynamic change of wireless network bandwidths, Quality of Service (QoS) scalable video streams need to be provided. This paper presents a system of content-adaptive streaming of instructional (lecture) videos over wireless networks for E-learning applications. We first provide a real-time content analysis method to detect and extract content regions from instructional videos, then apply a “leaking-video-buffer” model to adjust QoS of video streams dynamically based on video content. In content-adaptive video streaming, an adaptive feedback control scheme is also developed to transmit properly compressed video streams to video clients not only based on network bandwidth, but also based on video content and the preferences of users. Finally, we demonstrate the scalability and content adaptiveness of the proposed video streaming system with experimental results on several instructional videos.     

Content-adaptive wireless streaming of instructional videos

Video streaming over wireless networks is becoming increasingly important for a variety of applications. To accommodate the dynamic change of wireless network bandwidths, Quality of Service (QoS) scalable video streams need to be provided. This paper presents a system of content-adaptive streaming of instructional (lecture) videos over wireless networks for E-learning applications. We first provide a real-time content analysis method to detect and extract content regions from instructional videos, then apply a “leaking-video-buffer” model to adjust QoS of video streams dynamically based on video content. In content-adaptive video streaming, an adaptive feedback control scheme is also developed to transmit properly compressed video streams to video clients not only based on network bandwidth, but also based on video content and the preferences of users. Finally, we demonstrate the scalability and content adaptiveness of the proposed video streaming system with experimental results on several instructional videos.     

Supporting high-quality video streaming with SDN-based CDNs

Videos and other multimedia contents become increasing popular among users of the Internet nowadays. With the improvement of underlying infrastructure of the Internet, users are allowed to enjoy video contents with much higher quality than last decade. Content delivery networks (CDNs) are a type of content hosting solution that widely used across the Internet. Content providers offload the task of content hosting to CDN providers and redirect users’ requests to CDNs. Video contents, especially high quality videos at real-time has occupying a major part of the Internet traffic. It is challenging to handle such workloads even for a large- scale CDN. Load balancing algorithms are critical to address this issue. However, traditional load balancing algorithms such as round-robin and randomization are unaware of user side requirements. Therefore, it is not uncommon that requests for high-quality videos at real-time are not satisfied. In this paper, we try to fulfill such requests by integrating software-defined networking technology with CDN infrastructure. We also propose revised load balancing algorithms and develop simulations to verify our approaches. The results show that the proposed algorithms achieve much higher user satisfaction in bandwidth-idle environments.     

Modelling and analysis of non-cooperative peer-assisted VoD streaming in managed networks

The growing popularity of the Video on Demand service in the Internet Protocol Television environments and the demand for increased quality of the offered videos are becoming a serious threat for the service providers because the high amounts of video traffic are causing congestion in the delivery networks. One of the most acceptable approaches to solve this issue is the peer-assisted streaming, where the peers participate in the streaming process in order to alleviate the load on the streaming servers and in the core of the network. Although the reliability of the Peer-to-Peer service is considerably improved in the managed networks because of the control that the operators have over the clients’ Set-Top Boxes, the failures of the peers still cannot be completely eliminated. The operator can take advantage of the streaming and storage resources of the clients and use them for peer-assisted streaming only while they are watching a video, but not after they finish the streaming session because they may turn off their receiving devices until the next session. In this chapter, we address the issue of the failures of the peers in such environments and their influence on the traffic requested from the servers for providing uninterrupted video experience. For that purpose, we propose a precise mathematical tool for modelling a peer-assisted system for Video on Demand streaming in managed networks with non-cooperative peers, which may decide not to share their resources while they are not active. This tool calculates the performance of the system taking into consideration large variety of system parameters, including the failure probability and the time the peers spend until they decide to turn on the STB and join the network. As the results from the simulations verify the correctness of the mathematical model, we use it to analyse how the failures of the peers are affecting the system’s performance for different system parameters.     

基于仿真的P2P直播系统的研究

随着互联网技术的不断发展,基于P2P技术的网络视频直播和点播系统受到广大科研人员和用户的关注。与传统的C/S结构的视频播放系统相比,P2P直播系统可以用户提供更加流畅、更加清晰的视频服务。本文分析P2P直播系统的参数和工作原理,提供同构网中服务器负载的分析。     

Gravitational search algorithm based data scheduling for peer to peer video on demand system

Multimedia Tools and Applications - In peer to peer (P2P) video streaming systems, peers in network assist to forward the data to other peers without the interference of central servers. Video on...     

基于协作小小区与流行度预测的在线热点视频缓存更新策略

针对无线移动设备数量的指数增长使得异构协作小小区（SBS）将承载大规模的流量负载问题，提出了一种基于协作SBS与流行度预测的在线热点视频缓存更新方案（OVCRP）。首先，分析在线热点视频的流行度在短期内变化情况；然后，构建k近邻模型进行在线热点视频流行度的预测；最后，确定在线热点视频的缓存更新位置。为了选择合适的位置存放在线热点视频，以最小化总体传输时延为目标，建立数学模型，设计整数规划优化算法。仿真实验结果显示，与随机缓存（RANDOM）、最近最少使用（LRU）、最不经常使用（LFU）方案相比，OVCRP在平均缓存命中率和平均访问时延方面具有明显的优势，因此减轻了协作SBS的网络负担。     

Video recommendation over multiple information sources

Video recommendation is an important tool to help people access interesting videos. In this paper, we propose a universal scheme to integrate rich information for personalized video recommendation. Our approach regards video recommendation as a ranking task. First, it generates multiple ranking lists by exploring different information sources. In particular, one novel source user’s relationship strength is inferred through the online social network and applied to recommend videos. Second, based on multiple ranking lists, a multi-task rank aggregation approach is proposed to integrate these ranking lists to generate a final result for video recommendation. It is shown that our scheme is flexible that can easily incorporate other methods by adding their generated ranking lists into our multi-task rank aggregation approach. We conduct experiments on a large dataset with 76 users and more than 11,000 videos. The experimental results demonstrate the feasibility and effectiveness of our approach.     

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.     

A technique to circumvent SSL/TLS validations on iOS devices

SSL/TLS validations such as certificate and public key pinning can reinforce the security of encrypted communications between Internet-of-Things devices and remote servers, and ensure the privacy of users. However, such implementations complicate forensic analysis and detection of information disclosure; say, when a mobile app breaches user’s privacy by sending sensitive information to third parties. Therefore, it is crucial to develop the capacity to vet mobile apps augmenting the security of SSL/TLS traffic. In this paper, we propose a technique to bypass the system’s default certificate validation as well as built-in SSL/TLS validations performed in iOS apps. We then demonstrate its utility by analysing 40 popular iOS social networking, electronic payment, banking, and cloud computing apps.