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.     

Energy optimization for mobile video streaming via an aggregate model

Wireless video streaming on smartphones drains a significantly large fraction of battery energy, which is primarily consumed by wireless network interfaces for downloading unused data and repeatedly switching radio interface. In this paper, we propose an energy-efficient download scheduling algorithm for video streaming based on an aggregate model that utilizes user’s video viewing history to predict user behavior when watching a new video, thereby minimizing wasted energy when streaming over wireless network interfaces. The aggregate model is constructed by a personal retention model with users’ personal viewing history and the audience retention on crowd-sourced viewing history, which can accurately predict the user behavior of watching videos by balancing “user interest” and “video attractiveness”. We evaluate different users streaming multiple videos in various wireless environments and the results illustrate that the aggregate model can help reduce energy waste by 20 % on average. In addition, we also discuss implementation details and extensions, such as dynamically updating personal retention, balancing audience and personal retention, categorizing videos for accurate model.     

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.     

Practical design of a proxy agent to facilitate adaptive video streaming service across wired/wireless networks

Thanks to the growing of the wireless networks, the video streaming application becomes a ubiquitous joyful service. In a wireless communication network environment, the service traffic spans across the wired and wireless domains. In this article, we propose a practical design of a proxy agent - SPONGE (Stream Pooler Over a Network Graded Environment) sitting between the wireless User Equipments (UEs) and the video streaming server to facilitate the adaptive video streaming service across wired/wireless networks. To make the wireless streaming service more efficient, an input video session would be encoded as multiple qualities of video streams so that UEs with a similar receiving condition can share streams with the same service quality via SPONGE. SPONGE can alleviate the direct load on the original stream broadcasting server. Meanwhile, it can make each UE get an adaptive streaming service according to the network conditions of the UE by a reduced network condition feedback latency. Our theoretical analysis and simulation results show that SPONGE can help wireless streaming users get a smooth and better playback quality by a quick and accurate reaction to the network condition.     

Utility-oriented resource allocation for 360-degree video transmission over heterogeneous networks

Immersive media streaming, especially virtual reality (VR)/360-degree video streaming which is very bandwidth demanding, has become more and more popular due to the rapid growth of the multimedia and networking deployments. To better explore the usage of resource and achieve better video quality perceived by users, this paper develops an application-layer scheme to jointly exploit the available bandwidth from the LTE and Wi-Fi networks in 360-degree video streaming. This newly proposed scheme explores the saliency of video contents, prediction of users' view and channel status information to maximize the system's utility in a multi-RAN environment. It also determines the optimal association of users with multiple Wi-Fi access points (APs). Besides, a novel buffer strategy is proposed to mitigate the influence of short-time prediction problem for transmitting 360-degree videos in time-varying networks. The promising performance and low complexity of the proposed scheme and algorithms are validated in simulations with various 360-degree videos.     

Robust wireless sharing of internet video streams

The gateways are the performance bottleneck of wireless mesh access networks and thus alleviating stress on them is essential to making such wireless networks robust and scalable. Using proxy servers or wireless peer-to-peer streaming techniques can help reduce the gateway load. However, these techniques, because they are data caching methods, do not save wireless resources. We instead consider a communication-sharing approach in this paper. Traditional stream sharing solutions depend on cooperation with the video server. However, in the wireless access network it is difficult to cooperate with online video sites. To address this problem in wireless mesh access networks, we propose a distributed video sharing technique called Dynamic Stream Merging (DSM). DSM is able to improve the robustness of the access network without cooperation from the online video site or the users and has the intelligence to handle sudden spikes in demand for certain videos due to specific events, thereby preventing adverse effects to other daily wireless traffic. The technique can also leverage the 80:20 data access pattern, common for many video applications, to substantially increase the service throughput. We explain the DSM technique, present the system prototype, and discuss the experimental results.     

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。     

Enabling broadcast of user-generated live video without servers

We are witnessing the unprecedented popularity of User-Generated-Content (UGC) on the Internet. While YouTube hosts pre-recorded video clips, in near future, we expect to see the emergence of User-Generated Live Video, for which any user can create its own temporary live video channel from a webcam or a hand-held wireless device. Hosting a large number of UG live channels on commercial servers can be very expensive. Server-based solutions also involve various economic, copyright and content control issues between users and the companies hosting their content. In this paper, leveraging on the recent success of P2P video streaming, we study the strategies for end users to directly broadcast their own live channels to a large number of audiences without resorting to any server support. The key challenge is that end users are normally bandwidth constrained and can barely send out one complete video stream to the rest of the world. Existing P2P streaming solutions cannot maintain a high level of user Quality-of-Experience (QoE) with such a highly constrained video source. We propose a novel Layered P2P Streaming (LPS) architecture, to address this challenge. LPS introduces playback delay differentiation and constructs virtual servers out of peers to boost end users’ capability of driving large-scale video streaming. Through detailed packet-level simulations and PlanetLab experiments, we show that LPS enables a source with upload bandwidth slightly higher than the video streaming rate to stream video to tens of thousands of peers with premium quality of experience.     

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.     

Adaptive interface selection over cloud-based split-layer video streaming via multi-wireless networks

As mobile devices such as tablet PCs and smartphones proliferate, the online video consumption over a wireless network has been accelerated. From this phenomenon, there are several challenges to provide the video streaming service more efficiently and stably in the heterogeneous mobile environment. In order to guarantee the QoS of real-time HD video services, the steady and reliable wireless mesh is necessary. Furthermore, the video service providers have to maintain the QoS by provisioning streaming servers to respond the clients’ request of different video resolution. In this paper, we propose a reliable cloud-based video delivery scheme with the split-layer SVC encoding and real-time adaptive multi-interface selection over LTE and WiFi links. A split-layer video streaming can effectively scale to manage the required channels on each layer of various client connections. Moreover, split-layer SVC model brings streaming service providers a remarkable opportunity to stream video over multiple interfaces (e.g. WiFi, LTE, etc.) with a separate controlling based on their network status. Through the adaptive interface selection, the proposed system aims to ensure the maximizing video quality which the bandwidth of LTE/WiFi accommodates. In addition, the system offers cost-effective streaming to mobile clients by saving the LTE data consumption. In our system, an adaptive interface selection is developed with two different algorithms, such as INSTANT and EWMA methods. We implemented a prototype of mobile client based on iOS particularly by using iPhone5S. Moreover, we also employ the split-layer SVC encodes in streaming server-side as the add-on module to SVC reference encoding tool in a virtualized environment of KVM hypervisor. We evaluated the proposed system in an emulated and a real-world heterogeneous wireless network environments. The results show that the proposed system not only achieves to guarantee the highest quality of video frames via WiFi and LTE simultaneous connection, but also efficiently saves LTE bandwidth consumption for cost-effectiveness to client-side. Our proposed method provides the highest video quality without deadline misses, while it consumes 50.6% LTE bandwidth of ‘LTE-only’ method and 72.8% of the conventional (non-split) SVC streaming over a real-world mobile environment.     

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.     

An efficient hybrid push-pull methodology for peer-to-peer video live streaming system on mobile broadcasting social media

With the rapid growth of wireless communication technology, the availability of highly flexible and video-friendly mobile terminal platforms (such as smartphones and tablets), the emergence of major video content providers (like YouTube, Ustream, and PPTV, which provide a large catalog of attractive contents), Peer-to-Peer (P2P) live video streaming over the wireless and Internet is becoming more and more attractive to users. One of the main challenges is to provide a good quality of service though the dynamic behavior of the network. Traditionally, tree-based model uses a push method, that broadcaster transfers data to other users. This model has low start-up delay. However, there are two main problems in this method: if the bandwidth of an internal node is low, children nodes may lose data and when an internal node failure, other nodes can’t receive data until completing the recovery of the tree. On the other hand, mesh-based model uses a pull method, has low bandwidth of a neighbor node by pulling necessary data from a number of neighbor nodes. However, mesh-based model requires large buffers to support pull data from neighbor peers and there is an adjustment between minimum delay by sending pull request and overhead of whole system. So, both models have their own strengths and weaknesses. This paper proposes a new hybrid push-pull live P2P video streaming protocol called MobileCast that combines the benefits of pull and push mechanisms for live video delivery. We present new topology for P2P network with more stable and provide better video streaming quality. Our main goal is to minimize the network end-to-end delay, startup time, overhead, packet loss compared to the pure mesh networks, pure tree networks and provide a good quality of service though the dynamic behavior of the network.

     

Interest-suppression-based NDN live video broadcasting over wireless LAN

Named data networking (NDN) is a new Internet architecture that replaces today’s focus on where – addresses and hosts with what – the content that users and applications care about. One of NDN’s prominent advantages is scalable and efficient content distribution due to its native support of caching and multicast in the network. However, at the last hop to wireless users, often the WiFi link, current NDN implementation still treats the communication as multiple unicast sessions, which will cause duplicate packets and waste of bandwidth when multiple users request for the same popular content. WiFi’s built-in broadcast mechanism can alleviate this problem, but it suffers from packet loss since there is no MAC-layer acknowledgement as in unicast. In this paper, we develop a new NDN-based cross-layer approach called NLB for efficient and scalable live video streaming over wireless LAN. The core ideas are: using WiFi’s broadcast channel to deliver content from the access point to the users, a leader-based mechanism to suppress duplicate requests from users, and receiver-driven rate control and loss recovery. The design is implemented and evaluated in a physical testbed comprised of one software AP and 20 Raspberry Pi-based WiFi clients. While NDN with multiple unicast sessions or plain broadcast can support no more than ten concurrent viewers of a 1Mbps streaming video, NDN plus NLB supports all 20 viewers, and Received December 29, 2015; accepted April 28, 2016 E-mail: zhxp@tsinghua.edu.cn can likely support much more when present.     

Modeling and evaluating IPTV applications in WiMAX networks

Internet Protocol-based Television (IPTV) is a digital television service which delivers television content via an IP network. The rapid growth of wireless network technology in recent years has changed, the way people access the Internet. Adding mobility to IPTV can create a truly compelling ubiquitous service which spans different network domains and varied IP-enabled terminals and devices, such as set-top boxes, PCs and cell phones. However, extending IPTV service to wireless networks requires overcoming bandwidth bottlenecks and high packet loss rates. Following the IEEE 802.16 standard, worldwide interoperability for microwave access (WiMAX) features high data rates and large service coverage, offering a wireless broadband solution for IPTV services. While previous research has focused on creating a broadband IPTV service few studies have practically evaluated IPTV applications in a wireless broadband network environment. In this paper, we model and evaluate a common constant bit rate (CBR)¹ based IPTV application and an IPTV live streaming (PPStreaming)² application while retrieving IPTV content via a WiMAX network. We also use the NS2 simulation tool to evaluate the performance of these two IPTV applications. The evaluation metrics include latency, packet loss, data rate and throughput statistics when the two IPTV applications are run in the WiMAX network. ¹The simplest IPTV solution is to convey video content by CBR. IPTV operators and content delivery networks relay CBR streaming content to control the demand for network capacity. Broadcasters prefer CBR video as it conserves bandwidth resources, but CBR delivery can degrade video quality result in higher overall demand on network capacity. ²PPStreaming (also referred to as P2P streaming Internet TV) is a network for live media streaming. In principle it’s similar to BitTorrent (BT) in that it provides stable and smooth broadcast of TV programs to broadband users. Unlike traditional streaming media, PPStreaming adopts P2P-streaming technology and supports full-scale visits with tens of thousands of simultaneous users. Its client software can be used in the browser or as a standalone executable.     

Loopback: exploiting collaborative caches for large-scale streaming

In this paper, we propose a Loopback approach in a two-level streaming architecture to exploit collaborative client/proxy buffers for improving the quality and efficiency of large-scale streaming applications. At the upper level we use a content delivery network (CDN) to deliver video from a central server to proxy servers. At the lower level a proxy server delivers video with the help of collaborative client caches. In particular, a proxy server and its clients in a local domain cache different portions of a video and form delivery loops. In each loop, a single video stream originates at the proxy, passes through a number of clients, and finally is passed back to the proxy. As a result, with limited bandwidth and storage space contributed by collaborative clients, we are able to significantly reduce the required network bandwidth, I/O bandwidth, and cache space of a proxy. Furthermore, we develop a local repair scheme to address the client failure issue for enhancing service quality and eliminating most required repairing load at the central server. For popular videos, our local repair scheme is able to handle most of single-client failures without service disruption and retransmissions from the central server. Our analysis and simulations have shown the effectiveness of the proposed scheme.     

An efficient playout smoothing mechanism for layered streaming in P2P networks

Layered video streaming in peer-to-peer (P2P) networks has drawn great interest, since it can not only accommodate large numbers of users, but also handle peer heterogeneity. However, there’s still a lack of comprehensive studies on chunk scheduling for the smooth playout of layered streams in P2P networks. In these situations, a playout smoothing mechanism can be used to ensure the uniform delivery of the layered stream. This can be achieved by reducing the quality changes that the stream undergoes when adapting to changing network conditions. This paper complements previous efforts in throughput maximization and delay minimization for P2P streaming by considering the consequences of playout smoothing on the scheduling mechanisms for stream layer acquisition. The two main problems to be considered when designing a playout smoothing mechanism for P2P streaming are the fluctuation in available bandwidth between peers and the unreliability of user-contributed resources—particularly peer churn. Since the consideration of these two factors in the selection and scheduling of stream layers is crucial to maintain smooth stream playout, the main objective of our smoothing mechanism becomes the determination of how many layers to request from which peers, and in which order. In this work, we propose a playout smoothing mechanism for layered P2P streaming. The proposed mechanism relies on a novel scheduling algorithm that enables each peer to select appropriate stream layers, along with appropriate peers to provide them. In addition to playout smoothing, the presented mechanism also makes efficient use of network resources and provides high system throughput. An evaluation of the performance of the mechanism demonstrates that the proposed mechanism provides a significant improvement in the received video quality in terms of lowering the number of layer changes and useless chunks while improving bandwidth utilization.     

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.