QoS optimal real-time video streaming in distributed wireless image-sensing platforms

This paper provides a novel real-time video streaming method in distributed wireless image-sensing platforms. It consists of (1) a millimeter-wave (mmW)-based multi-hop routing optimization for real-time video streaming, (2) wireless image-sensing platforms by using the high-efficiency video coding. A mmW wireless communication is a promising technology for increasing capacity in next-generation wireless systems. However, the weakness of mmW signals to (1) do long-distance transmission and (2) survive in non-line-of-sight environments makes the mmW networks need a multi-hop relaying. Thus, this paper focuses on the maximization of video transmission quality of service (QoS) that makes the optimization problem different from the conventional sum-rate maximization. Specifically, this paper develops an algorithm that optimizes the summation of QoS of the individual wireless transmission of different video streams, subject to constraints of separate streams (i.e., minimum requirements in each stream). Experimental results show the proposed approach presents 32 % better performance with 1.84 dB gain in Y-peak signal-to-noise ratio than the widely used max–min flow routing that is generally considered in QoS-sensitive video streaming applications.     

Real-time video streaming over multipath in multi-hop wireless networks

Given the limited wireless link throughput, high loss rate, and varying end-to-end delay, supporting video applications in multi-hop wireless networks becomes a challenging task. Path diversity exploits multiple routes for each session simultaneously, which achieves higher aggregated bandwidth and potentially decreases delay and packet loss. Unfortunately, for TCP-based video streaming, naive load splitting often results in inaccurate estimation of round trip time (RTT) and packet reordering. As a result, it can suffer from significant instability or even throughput reduction, which is also validated by our analysis and simulation in multi-hop wireless networks. To make real-time TCP-based streaming viable over multi-hop wireless networks, we propose a novel cross-layer design with a smart traffic split scheme, namely, multiple path retransmission (MPR). MPR differentiates the original data packets and the retransmitted packets and works with a novel QoS-aware multi-path routing protocol, QAOMDV, to distribute them separately. MPR does not suffer from the RTT underestimation and extra packet reordering, which ensures stable throughput improvement over single-path routing. Through extensive simulations, we further demonstrate that, as compared with state-of-the-art multi-path protocols, our MPR with QAOMDV noticeably enhances the TCP streaming throughput and reduces bandwidth fluctuation, with no obvious impact to fairness.     

Loss tolerant video streaming authentication in heterogeneous wireless networks

Multicast video streaming in heterogeneous networks undergoes to very different physical constraints, in fact, such networks are characterized by different QoS parameters, involving one or more transcoding process between the sender and the receivers. Video streaming authentication algorithms must be robust to transcoding processes and must guarantee the copyright of the video owner/producer. We propose a real-time video streaming authentication algorithm that can guarantee the copyright of the video owner and that we prove to be robust to packet loss and transcoding processes.     

Load balancing for video streaming services in hierarchical wireless networks

Wireless networks have focused on voice call services or wireless Internet access services. These days, the application service domain of wireless networks is rapidly expanding, and a wide variety of new services is emerging. Video streaming service is one of the most promising examples, evidenced by 3GPP’s MBMS (Multimedia Broadcast Multicast Service) and IMS (IP Multimedia Subsystem). In this paper, we consider the provision of video streaming services in hierarchical wireless networks with multiple layers of cells. We particularly focus on optimal load balancing among the cells, aiming at the minimization of frame drop ratio for given video streaming sessions. From this objective function, we derive the optimal load balancing condition. Load balancing is essentially the issue of which users are assigned to which cell, i.e., the user assignment problem. In our user assignment algorithm, we compute thresholds to divide users into groups according to the user characteristics, and map the user groups to proper cells. The optimal load balancing condition can be reached by adaptively adjusting the threshold at run time. This process does not require prior knowledge about the system status, such as the system capacity or user traffic requests, which warrants the practicality of the proposed scheme. Via simulations, we demonstrate that the proposed scheme achieves optimal load balancing in various realistic environments.     

Version-vector based video data online cloud backup in smart campus

As a kind of popular multimedia service, the video business in smart campus is fully developed, and the data volume is experiencing a continuous tremendous growth. However, the video data storage also needs to cope with many security threats resulted from some uncertainly factors. Recently, data backup has been credited as an important method to address these issues. Yet, how to improve the efficiency of video data online backup remains a critical challenge. In the context of cloud computing, a version-vector based video data online backup system is proposed in this paper, which fully implements three important functions, e.g., backup, indexing and recovery. At the time of the client backuping the data to the server, the version vectors of files and data chunks are produced as metadata. Accordingly, only through exchanging a small amount of metadata between the data nodes, it can identify the redundant data such that breaking the limitations on file-level deduplication and data-chunk-level deduplication in the existing backup systems, and can further improve the performance of backup, indexing and recovery. The conducted experiments have been shown to demonstrate the efficiency.

     

Unsupervised deep learning for real-time assessment of video streaming services

Multimedia Tools and Applications - Evaluating quality of experience in video streaming services requires a quality metric that works in real time and for a broad range of video types and network...     

SSVP: A congestion control scheme for real-time video streaming

In this paper, we present a new end-to-end protocol, namely Scalable Streaming Video Protocol (SSVP), which operates on top of UDP and is optimized for unicast video streaming applications. SSVP employs Additive Increase Multiplicative Decrease (AIMD)-based congestion control and adapts the sending rate by properly adjusting the inter-packet-gap (IPG). The smoothness-oriented modulation of AIMD parameters and IPG adjustments reduce the magnitude of AIMD oscillation and allow for smooth transmission patterns, while TCP-friendliness is maintained. Our experimental results demonstrate that SSVP eventually adapts to the vagaries of the network and achieves remarkable performance on real-time video delivery. In the event where awkward network conditions impair the perceptual video quality, we investigate the potential improvement via a layered adaptation mechanism that utilizes receiver buffering and adapts video quality along with long-term variations in the available bandwidth. The adaptation mechanism sends a new layer based on explicit criteria that consider both the available bandwidth and the amount of buffering at the receiver, preventing wasteful layer changes that have an adverse effect on user-perceived quality. Quantifying the interactions of SSVP with the specific adaptation scheme, we identify notable gains in terms of video delivery, especially in the presence of limited bandwidth.     

无线Mesh网络中的P2P流媒体性能评估

通过搭建基于无线Mesh网络的P2P流媒体点播测试平台,对影响无线Mesh网络中P2P流媒体性能的流媒体编解码方式、编码速率、数据转发路径的选择和跳数四个因素进行了测试。实验结果表明,采用H.264编解码标准更适合无线Mesh网络中流媒体的传输;编码速率必须不大于网络连接速率才能获得高视频质量;P2P技术可以抵抗10%的丢包对视频质量的影响,比采用非P2P技术在视频的前1 000帧视频质量平均高出3 dB;由于P2P技术带来的流间干扰的影响使得1 000帧以后视频质量下降了6 dB,严重影响了流媒体性能;无线Mesh网络的传输能力随着跳数的增加而减弱,但是流媒体质量并未随着跳数的增加而降低。     

A buffered-bandwidth approach for supporting real-time video streaming over cellular networks

In this paper, we study the admission and bandwidth allocation problems in real-time video streaming in a cellular network. Admission control in a cellular network is a complex issue due to the mobility of the clients, and the additional workload imposed by incoming clients could exceed the network capacity of a cell and seriously degrade the quality of services provided to the resident clients. To minimize the number of forced terminations of real-time video playback, we incorporate the notion of buffered bandwidth in the admission test for handoff client. Using this approach, we can balance the video workload among adjacent cells to minimize the impact of overloading as the result of handoff operations. We also examine techniques to maintain fairness in services especially under overload situations even though the requested videos from various types of clients could generate very different workload. Simulation experiments confirm the effectiveness of our approach compared to widely used schemes.     

基于3G的无线实时视频监控系统设计

设计并实现了一种基于3GWCDMA无线网络的远程视频实时监控系统,该系统以Cypress公司的CY8C52芯片、TI公司的TMS320DM642芯片以及华为公司的EM770W通信模块为核心,融合了嵌入式、视频压缩、无线通信等技术,实现了视频的采集、压缩、传输、解码、显示等功能。该系统还对压缩模块中DSP编码选项和MPEG-4算法进行了优化,减少了运算量,提高了编码效率,使系统的信号处理能力得到了很大改善。     

SIP and SDP based content adaptation during real-time video streaming in Future Internets

Multimedia Tools and Applications - Future Internet technology supports content adaptation to improve the QoS / QoE in a heterogeneous environment. The adaptation process removes partial data to...     

Real-time video transport optimization using streaming agent over 3G wireless networks

Feedback adaptation has been the basis for many media streaming schemes, whereby the media being sent is adapted in real time according to feedback information about the observed network state and application state. Central to the success of such adaptive schemes, the feedback must: 1) arrive in a timely manner and 2) carry enough information to effect useful adaptation. In this paper, we examine the use of feedback adaptation for media streaming in 3G wireless networks, where the media servers are located in wired networks while the clients are wireless. We argue that end-to-end feedback adaptation using only information provided by 3G standards is neither timely nor contain enough information for media adaptation at the server. We first show how the introduction of a streaming agent (SA) at the junction of the wired and wireless network can be used to provide useful information in a timely manner for media adaptation. We then show how optimization algorithms can be designed to take advantage of SA feedbacks to improve performance. The improvement of SA feedbacks in peak signal-to-noise ratio is significant over nonagent-based systems.     

Toward online node classification on streaming networks

The proliferation of networked data in various disciplines motivates a surge of research interests on network or graph mining. Among them, node classification is a typical learning task that focuses on exploiting the node interactions to infer the missing labels of unlabeled nodes in the network. A vast majority of existing node classification algorithms overwhelmingly focus on static networks and they assume the whole network structure is readily available before performing learning algorithms. However, it is not the case in many real-world scenarios where new nodes and new links are continuously being added in the network. Considering the streaming nature of networks, we study how to perform online node classification on this kind of streaming networks (a.k.a. online learning on streaming networks). As the existence of noisy links may negatively affect the node classification performance, we first present an online network embedding algorithm to alleviate this problem by obtaining the embedding representation of new nodes on the fly. Then we feed the learned embedding representation into a novel online soft margin kernel learning algorithm to predict the node labels in a sequential manner. Theoretical analysis is presented to show the superiority of the proposed framework of online learning on streaming networks (OLSN). Extensive experiments on real-world networks further demonstrate the effectiveness and efficiency of the proposed OLSN framework.     

Examining students’ online interaction in a live video streaming environment using data mining and text mining

This study analyses the online questions and chat messages automatically recorded by a live video streaming (LVS) system using data mining and text mining techniques. We apply data mining and text mining techniques to analyze two different datasets and then conducted an in-depth correlation analysis for two educational courses with the most online questions and chat messages respectively. The study found the discrepancies as well as similarities in the students’ patterns and themes of participation between online questions (student–instructor interaction) and online chat messages (student–students interaction or peer interaction). The results also identify disciplinary differences in students’ online participation. A correlation is found between the number of online questions students asked and students’ final grades. The data suggests that a combination of using data mining and text mining techniques for a large amount of online learning data can yield considerable insights and reveal valuable patterns in students’ learning behaviors. Limitations with data and text mining were also revealed and discussed in the paper.     

More than watching: An empirical and experimental examination on the impacts of live streaming user-generated video consumption

Live streaming user-generated video (UGV), a nascent format of crowdsourced content, has grown massive popularity among social media users and is believed to have substantial potential business influences. However, industry practitioners express concerns regarding this new form on social media platforms and the influences of live streaming UGV consumption lack research. Motivated such, through a uniquely merged dataset from the video game industry, we conduct a series of panel time-series empirical analyses to investigate the business value of live streaming UGV consumption. Further, we propose a conceptual framework based on the sense of community literature to understand the consequences of live streaming UGV consumption and verify it through several online experiments. This research contributes to the IS literature by shedding light on the influences of emergent crowdsourced content, providing a conceptual framework to existing literature, and offering rich managerial implications and guidelines to managers regarding utilizing live streaming UGVs.     

Efficient link scheduling for online admission control of real-time traffic in wireless mesh networks

Link scheduling is used in wireless mesh networks (WMNs) to guarantee interference-free transmission on the shared wireless medium in a time division multiple access approach. Several papers in the literature address the problem of link scheduling guaranteeing a minimum throughput to the flows traversing the WMN. However, none of the existing works address the problem of computing a schedule that guarantees that pre-specified end-to-end delay constraints are met. In this paper, we make a first step forward in this direction by defining a link scheduling algorithm that works in sink-tree WMNs, i.e. those whose traffic is routed towards a common sink (i.e. the Internet gateway). Our iterative algorithm exploits a delay-based admission control procedure, devised through Network Calculus, which solves an optimization problem and tests the feasibility of a schedule from the point of view of delay guarantees. Thanks to a clever solution approach for the optimization problem, the iterative algorithm computes feasible solutions in affordable times for networks of several tens of nodes, and is thus amenable to online admission control of real-time traffic.