Energy-Efficient Joint Caching and Transcoding for HTTP Adaptive Streaming in 5G Networks with Mobile Edge Computing

With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G networks has been widely studied. Although many works have been done, most of the existing works focus on the issues of network resource utilization or the quality of experience (QoE) promotion, while the energy efficiency is largely ignored. In this paper, different from previous works, in order to realize the energy efficiency for video transmission in MEC-enhanced 5G networks, we propose a joint caching and transcoding schedule strategy for HTTP adaptive video streaming transmission by taking the caching and transcoding into consideration. We formulate the problem of energy-efficient joint caching and transcoding as an integer programming problem to minimize the system energy consumption. Due to solving the optimization problem brings huge computation complexity, therefore, to make the optimization problem tractable, a heuristic algorithm based on simulated annealing algorithm is proposed to iteratively reach the global optimum solution with a lower complexity and higher accuracy. Finally, numerical simulation results are illustrated to demonstrated that our proposed scheme brings an excellent performance.

Video transcoding for adaptive bitrate streaming over edge-cloud continuum

Video transcoding is to create multiple representations of a video for content adaptation.It is deemed as a core technique in Adaptive BitRate(ABR)streaming.How to manage video transcoding affects the performance of ABR streaming in various aspects,including operational cost,streaming delays,Quality of Experience(QoE),etc.Therefore,the problems of implementing video transcoding in ABR streaming must be systematically studied to improve the overall performance of the streaming services.These problems become more worthy of investigation with the emergence of the edge-cloud continuum,which makes the resource allocation for video transcoding more complicated.To this end,this paper provides an investigation of the main technical problems related to video transcoding in ABR streaming,including designing a rate profile for video transcoding,providing resources for video transcoding in clouds,and caching multi-bitrate video contents in networks,etc.We analyze these problems from the perspective of resource allocation in the edge-cloud continuum and cast them into resource and Quality of Service(QoS)optimization problems.The goal is to minimize resource consumption while guaranteeing the QoS for ABR streaming.We also discuss some promising research directions for the ABR streaming services.

Resource Allocation and User Association for HTTP Adaptive Streaming in Heterogeneous Cellular Networks with Small Cells

Video streaming,especially hypertext transfer protocol based(HTTP) adaptive streaming(HAS) of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for the mobile networks.Although some works have been done for video streaming delivery in heterogeneous cellular networks,most of them focus on the video streaming scheduling or the caching strategy design.The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored.In this paper,the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied,we model the optimization problem as a mixed integer programming problem.And to reduce the computational complexity,an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved.Then we use the many-to-one matching model to analyze the user association problem,and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed.Finally,extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.

MEC-Assisted Flexible Transcoding Strategy for Adaptive Bitrate Video Streaming in Small Cell Networks

Adaptive bitrate video streaming(ABR)has become a critical technique for mobile video streaming to cope with time-varying network conditions and different user preferences.However,there are still many problems in achieving high-quality ABR video streaming over cellular networks.Mobile Edge Computing(MEC)is a promising paradigm to overcome the above problems by providing video transcoding capability and caching the ABR video streaming within the radio access network(RAN).In this paper,we propose a flexible transcoding strategy to provide viewers with low-latency video streaming services in the MEC networks under the limited storage,computing,and spectrum resources.According to the information collected from users,the MEC server acts as a controlling component to adjust the transcoding strategy flexibly based on optimizing the video caching placement strategy.Specifically,we cache the proper bitrate version of the video segments at the edge servers and select the appropriate bitrate version of the video segments to perform transcoding under jointly considering access control,resource allocation,and user preferences.We formulate this problem as a nonconvex optimization and mixed combinatorial problem.Moreover,the simulation results indicate that our proposed algorithm can ensure a low-latency viewing experience for users.

Adaptive peer-to-peer streaming with MutualCast

Application Layer Multicast (ALM) can greatly reduce the load of a server by leveraging the outgoing bandwidth of the participating nodes. However, most proposed ALM schemes become quite complicated and lose bandwidth efficiency if they try to deal with networks that are significantly heterogeneous or time-varying. In earlier work, we proposed MutualCast, an ALM scheme with fully connected mesh that quickly adapts to the time-varying networks, while achieving provably optimal throughput performance. In this paper, we study how MutualCast can be paired with adaptive rate control for streaming media. Specifically, we combine Optimal Rate Control (ORC), our earlier control-theoretical framework for quality adaptation, with the MutualCast delivery scheme. Using multiple bit rate video content, we show that the proposed system can gracefully adjust the common quality received at all the nodes while maintaining a continuous streaming experience at each, even when the network undergoes severe, uncorrelated bandwidth fluctuations at different peer nodes.

Network-adaptive HD MPEG-2 video streaming with cross-layered channel monitoring in WLAN

In this paper, we propose a practical design and implementation of network-adaptive high definition (HD) MPEG-2 video streaming combined with cross-layered channel monitoring (CLM) over the IEEE 802.11a wireless local area network (WLAN). For wireless channel monitoring, we adopt a cross-layered approach, where an access point (AP) periodically measures lower layers such as medium access control (MAC) and physical (PHY) transmission information (e.g., MAC layer loss rate) and then sends the monitored information to the streaming server application. The adaptive streaming server with the CLM scheme reacts more quickly and efficiently to the fluctuating wireless channel than the end-to-end application-layer monitoring (E2EM) scheme. The streaming server dynamically performs priority-based frame dropping to adjust the sending rate according to the measured wireless channel condition. For this purpose, the proposed streaming system nicely provides frame-based prioritized packetization by using a real-time stream parsing module. Various evaluation results over an IEEE 802.11a WLAN testbed are provided to verify the intended Quality of Service (QoS) adaptation capability. Experimental results showed that the proposed system can mitigate the quality degradation of video streaming due to the fluctuations of time-varying channel.

Automating QoS and QoE Evaluation of HTTP Adaptive Streaming Systems

Streaming audio and video content currently accounts for the majority of the Internet traffic and is typically deployed over the top of the existing infrastructure. We are facing the challenge of a plethora of media players and adaptation algorithms showing different behavior but lacking a common framework for both objective and subjective evaluation of such systems. This paper aims to close this gap by proposing such a framework, describing its architecture, providing an example evaluation, and discussing open issues.

Design and Implementation of an Adaptive Feedback Queue Algorithm over Open Flow Networks

The concurrent presence of different types of traffic in multimedia applications might aggravate a burden on the underlying data network, which is bound to affect the transmission quality of the specified traffic. Recently, several proposals for fulfilling the quality of service(QoS) guarantees have been presented. However, they can only support coarse-grained QoS with no guarantee of throughput, jitter, delay or loss rate for different applications. To address these more challenging problems, an adaptive scheduling algorithm for Parallel data Processing with Multiple Feedback(PPMF) queues based on software defined networks(SDN) is proposed in this paper, which can guarantee the quality of service of high priority traffic in multimedia applications. PPMF combines the queue bandwidth feedback mechanism to realise the automatic adjustment of the queue bandwidth according to the priority of the packet and network conditions, which can effectively solve the problem of network congestion that has been experienced by some queues for a long time. Experimental results show PPMF significantly outperforms other existing scheduling approaches in achieving 35--80% improvement on average time delay by adjusting the bandwidth adaptively, thus ensuring the transmission quality of the specified traffic and avoiding effectively network congestion.

An embedded packet train and adaptive FEC scheme for effective video adaptation over wireless broadband networks

With the rapid growth of wireless broadband technologies, such as WLAN and WiMAX, quality streaming video contents are available through portable devices anytime, anywhere. The layered multicast system using scalable video codecs has been proposed as an efficient architecture for video dissemination taking account of user and link diversities. However, in the wired/wireless combined best-effort based heterogeneous IP networks which provide more fluctuation in available bandwidth and end-to-end delay, the performance of streaming systems has been greatly degraded due to frequent packet loss, resulting from either wired congestion or wireless fading/shadowing. In this paper, we present a real-time embedded packet train probing scheme for estimating end-to-end available bandwidth so as to accomplish effective congestion and error control. This is facilitated by effective classification of packet loss sources, delay trend detection algorithm and flexible transmission rate of packets. Under the proper wireless channel modelling and estimation, our layered structure can allow appropriate subscription of video layers and adaptively insert necessary amount of forward error correction (FEC) packets so as to achieve QoS optimized system for scalable video multicasting.

Dynamic multimedia stream adaptation and rate control for heterogeneous networks

Dynamic adaptation of multimedia content is seen as an important feature of next generation networks and pervasive systems enabling terminals and applications to adapt to changes in e.g. context, access network, and available Quality-of-Service(QoS) due to mobility of users, devices or sessions. We present the architecture of a multimedia stream adaptation service which enables communication between terminals having heterogeneous hardware and software capabilities and served by heterogeneous networks. The service runs on special content adaptation nodes which can be placed at any location within the network. The flexible structure of our architecture allows using a variety of different adaptation engines. A generic transcoding engine is used to change the codec of streams. An MPEG-21 Digital Item Adaptation (DIA) based transformation engine allows adjusting the data rate of scalable media streams. An intelligent decision-taking engine implements adaptive flow control which takes into account current network QoS parameters and congestion information. Measurements demonstrate the quality gains achieved through adaptive congestion control mechanisms under conditions typical for a heterogeneous network.

Smart Instrumentation in Video Streams Using Real-time Bit-rate Adaptation

With the proliferation of video traffic across the Internet and wireless networks,various compression standards for videos have emerged over the past two decades.Among them,Motion Joint Photographic Expects Group(M-JPEG)offers the advantages of no frame-to-frame error propagation,less computation cost,and achieving a short latency in both encoding and decoding.However,the bit-rate of M-JPEG stream is variable due to its dynamic frame size,and that leads to adverse outcomes such as inducing different quality-of-service(QoS)grades from servers and networks and inducing disturbances in a real-time network environment.This paper proposes a novel approach that can control bit-rate and also the individual frame size of M-JPEG video stream in real-time.Experimental results are provided to show that the proposed approach is amenable to direct,straightforward implementation and yet outperforms similar existing approaches in regulating the bit-rate and the frame size of M-JPEG streams.

Adaptive progressive download based on the MPEG-4 file format

In this paper we describe how progressive download and adaptive streaming can be combined into a simple and efficient streaming framework. Based on the MPEG-4 file format (MP4) we use HTTP for transport and argue that these two components are sufficient for specifying an open streaming architecture. The client selects appropriate chunks from the MP4 file to be transferred based on (1) the header information (i.e. the 'moov' box) in the first part of the file and (2) his observation of network throughput. The framework is completely client driven which allows for better server scalability and reduces signalling overhead. We discuss architecture and implementation issues such as complexity, interoperability and scalability and compare to 3GPP PSS Re1-6 adaptive streaming when appropriate. Measurements from a simple MP4/HTTP streaming client are presented showing that appropriate chunks are selected such that increased reliability is achieved.

使用Spark Streaming的自适应实时DDoS检测和防御技术

分布式拒绝服务(distributed denial of service,DDoS)攻击是重要的安全威胁,网络速度的不断提高给传统的检测方法带来了新的挑战。以Spark等为代表的大数据处理技术,给网络安全的高速检测带来了新的契机。提出了一种基于Spark Streaming框架的自适应实时DDoS检测防御技术,通过对滑动窗口内源簇进行分组,并根据与各分组内源簇比例的偏差统计,检测出DDoS攻击流量。通过感知合法的网络流量,实现了对DDoS攻击的自适应快速检测和有效响应。实验结果表明,该技术可极大地提升检测能力,为保障网络服务性能和安全检测的可扩展性提供了一种可行的解决方案。

HoliAdapt——数据流中一种适应性查询处理策略

针对数据流上连续查询处理的特征,为适应流数据特征以及执行环境的易变因素,该文从选择率和执行时间的角度出发,考虑内存使用量和输出延迟适应性因素,提出一种适应性的查询处理策略—HoliAdapt。该策略基于查询窗口动态地收集统计信息,利用数学方法不断地优化查询计划,通过核心调度方法,对操作符进行适应性的调度。实验证明该策略有效地减少时间延迟和内存使用量,最终提高系统查询的效率。

Quality Assessment and Measurement for Internet Video Streaming

Benefiting from the improvements of Internet infrastructure and video coding technology, online video services are becoming a new favorite form of video entertainment.However, most of the existing video quality assessment methods are designed for broadcasting/cable televisions and it is still an open issue how to assess and measure the quality of online video services. In this paper, we survey the state-of-the-art video streaming technologies, and present a framework of quality assessment and measurement for Internet video streaming. This paper introduces several metrics for user's quality of experience(QoE).These QoE metrics are classified into two categories: objective metrics and subjective metrics. It is different for service participators to measure objective and subjective metrics.The QoE measurement methodologies consist of client-side, server-side, and in-network measurement.

Two-Phase Rate Adaptation Strategy for Improving Real-Time Video QoE in Mobile Networks

With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.

Delivering audiovisual content with MPEG-21-enabled cross-layer QoS adaptation

Future multimedia communication systems have to support the user’s needs, the terminal capabilities, the content specification and the underlying networking technologies. The related protocols and applications must be designed from this integration perspective in a cross-layer centric manner. In this paper, we propose an implementation of a streaming service (e.g., Television over IP service) with a unified QoS management concept that enables an IP driven integration of different system components (terminal, user, content, and network). The MPEG-21 framework is used to provide a common support for imple- menting and managing the end-to-end QoS. The main focus of this paper is on the architecture design, protocols specification and implementation evaluation. Performance evaluations using PSNR and SSIM objective video quality metrics show the benefit of the proposed MPEG-21-enabled cross-layer adaptation.

An optimal quality adaptation mechanism for end-to-end FGS video transmission

In this paper, we propose a novel optimal quality adaptation algorithm for MPEG-4 fine granular scalability (FGS)stream over wired network. Our algorithm can maximize perceptual video quality by minimizing video quality variation and increasing available bandwidth usage rate. Under the condition that the whole bandwidth evolution is known, we design an optimal algorithm to select layer. When the knowledge of future bandwidth is not available, we also develop an online algorithm based on the optimal algorithm. Simulation showed that both optimal algorithm and online algorithm can offer smoothed video quality evolution.

基于自适应深度集成网络的概念漂移收敛方法

概念漂移是流数据挖掘领域中的一个重要且具有挑战性的难题.然而,目前的方法大多仅能够处理线性或简单的非线性映射,深度神经网络虽然有较强的非线性拟合能力,但在流数据挖掘任务中,每次只能在新得到的1个或一批样本上进行训练,学习模型难以实时调整以适应动态变化的数据流.为解决上述问题,将梯度提升算法的纠错思想引入含概念漂移的流数据挖掘任务之中,提出了一种基于自适应深度集成网络的概念漂移收敛方法(concept drift convergence method based on adaptive deep ensemble networks,CD_ADEN).该模型集成多个浅层神经网络作为基学习器,后序基学习器在前序基学习器输出的基础上不断纠错,具有较高的实时泛化性能.此外,由于浅层神经网络有较快的收敛速度,因此所提出的模型能够较快地从概念漂移造成的精度下降中恢复.多个数据集上的实验结果表明,所提出的CD_ADEN方法平均实时精度有明显提高,相较于对比方法,平均实时精度有1%~5%的提升,且平均序值在7种典型的对比算法中排名第一.说明所提出的方法能够对前序输出进行纠错,且学习模型能够快速地从概念漂移造成的精度下降中恢复,提升了在线学习模型的实时泛化性能.

全景视频基于块的视口自适应传输方案综述

全景视频由于独特的沉浸式、交互式体验受到广泛关注。全景视频传输所需的高带宽、低时延给现有网络传输系统带来了挑战。基于tile(块)的视口自适应传输可以有效缓解全景视频所带来的传输压力,成为当前的主流方案和研究热点。通过分析基于tile的视口自适应传输方案的研究现状和发展趋势,对该传输方案的两个重要模块,即视口预测与码率分配进行论述,从不同视角归纳总结相关领域的方法。首先,基于全景视频传输框架对相关技术进行阐明;其次,从主、客观两个维度分别介绍评估传输系统性能的用户体验质量的指标;再后,分别从视口预测、码率分配两方面进行归纳,系统梳理经典的研究方法;最后,基于当前研究现状讨论全景视频传输的未来发展趋势。