This article presents a novel, end-to-end, qualityaware optimization framework for multimedia clouds, where path selection mechanisms are exploited in conjunction with media optimization in order to support multimedia delivery in a quality-aware manner. As wireless data traffic worldwide is characterized by exponential growth, with the most prominent part being multimedia services, consumers get in the challenging position to compete for the limited wireless network resources. Cloud technologies and especially Software-Defined Networking is the perfect candidate technology in order to provide an elastic, dynamic provisioning of network resources that adapt to a highly changing environment, where application requirements and data volumes vary over time. This work combines the selection of the optimum path in the core network with quality-aware media adaptation based on the current conditions of the wireless access network. Thus the proposed framework achieves efficient network resources utilization in an end-to-end fashion

Athanasopoulos, D

Dagiuklas, A.

Lykourgiotis, A

Politis, I

Tselios, C

LSBU Research Open

1End-to-end quality aware optimization formultimedia cloudsDimitris Athanasopoulos∗, Ilias Politis∗, Asimakis Lykourgiotis∗, Christos Tselios† and Tasos Dagiuklas∗∗Hellenic Open University, Patras, 26335, Greece, Email: {dathanasopoulos, ipolitis, alykourgiotis,dagiuklas}@eap.gr†University of Patras, Rio 26500, Greece, Email: tselios@ece.upatras.grAbstract—This article presents a novel, end-to-end, quality-aware optimization framework for multimedia clouds, wherepath selection mechanisms are exploited in conjunction withmedia optimization in order to support multimedia delivery ina quality-aware manner. As wireless data traffic worldwide ischaracterized by exponential growth, with the most prominentpart being multimedia services, consumers get in the challengingposition to compete for the limited wireless network resources.Cloud technologies and especially Software-Defined Networkingis the perfect candidate technology in order to provide an elastic,dynamic provisioning of network resources that adapt to a highlychanging environment, where application requirements and datavolumes vary over time. This work combines the selection ofthe optimum path in the core network with quality-aware mediaadaptation based on the current conditions of the wireless accessnetwork. Thus the proposed framework achieves efficient networkresources utilization in an end-to-end fashion.I. INTRODUCTIONAs multimedia and real-time applications are migratingon-line, new network architectures and novel paradigms forservices and networks convergence, need to be described.Evidently, both Quality of Service (QoS) and Quality of Expe-rience (QoE) need to be optimised and maintained by contentproviders and network operators [1], [2]. Currently, there aretechnological solution that could be deployed towards thisend, including Content Delivery Network (CDN), Data CenterVirtualization (DCV) and bulk data processing. Although therequirements for processing power, resource management andrequired bandwidth for such solutions to be utilised anddeployed in large scales is considerable, Cloud infrastructurehave recently arisen as the preferred solution.This new environment, which is based on the softwareand hardware virtualization, has advantages from both themultimedia content and the network providers. The former willbe driven to offer applications on demand, massively scalableand always on, in order to satisfy users with heterogeneouscapabilities and characteristics, while following a pay-as-you-grow business model to minimise the costs. The latter, willexploit the users’ ability to store, process and adapt multimediadata in the cloud and instantiate cloud functionalities thatusers may access with a minimum latency and user experiencerequirements [3], [4]. However, existing cloud computing tech-nologies are not optimised for multimedia and video streamingservices. Currently, the available computing infrastructures arestriving to cope with the demand of multiple video flowhandling in terms of encoding, processing and streaming.Software-defined networks (SDNs) are a new frameworkof networking which abstracts underlying network infrastruc-ture and provides access through software and applicationsallowing significant changes on network behaviour [5], [6].This introduces the ability of applying business logic tonetwork behaviour in a dynamic way and facilitates data flowcontrol through constant (if not real-time) modification, sincethousands of virtual machines and sizeable applications canbe deployed. The availability of open APIs for managementand data plane control, like OpenFlow [7], OpenStack [8] orOpenNaaS [9], provide an additional degree of integration ofNetwork Functions Virtualization (NFV) and cloud infrastruc-ture.This paper presents a novel optimization framework, whichcombines SDN’s dynamic video flow adaptation via a proposedQoS-aware path switching scheme, along with a media awareoptimization algorithm that selectively shapes the video trafficaccording to the access network conditions. The proposedframework is evaluated based on test-bed experimentationsusing video sequences with various coding and content charac-teristics. The obtained results in terms of network throughputand perceived video quality are compared against a non-optimised set-up that acts as the baseline scenario.The rest of the paper is structured as follows. Section IIdescribes the proposed SDN path selection algorithm, whilein Section III the media aware optimisation scheme is brieflydiscussed. Section IV includes the experimental set-up andan evaluation on the obtained results. The paper concludesin Section V.II. SDN BASED VIDEO STREAMING OPTIMIZATIONSDN is able to provide high flexibility, unprecedentedprogrammability and dynamic reconfigurability of the networkresources. Along with the ever increasing demand for videotraffic over wireless and wired All-IP networks, renders SDNthe most suitable candidate technology to provide elasticityand dynamic provisioning of network resources in order tocope with the varying video streaming characteristics.To achieve dynamic and just-in-time decision for QoS pro-vision over the SDN network,a real-time network monitoringand analytics tool was designed. The tool transparently andefficiently collects link-based (i.e., maximum theoretical band-width, topology information, etc.) and traffic-based metrics2Fig. 1. Proposed SDN based video streaming optimization and QoS-awarepath selection algorithm(i.e., packet loss, path delay, link throughout, link utilization,etc.) from the network. Moreover, a flow-based routing algo-rithm is proposed for differentiating and prioritizing scalablevideo coding (SVC) base layer flows, over the SDN. Thereforethe video traffic flows are differentiated between base layerstreams and enhancement layer streams and then prioritizedaccording to their impact on the perceptual quality of thereceived video. The proposed algorithm of Fig. 1 determinesappropriate paths between one or more sources to one or moredestinations, which satisfy the constraints of the video flowsmarked as important.III. MEDIA AWARE PROXYMAP is defined as a transparent user-space module respon-sible for low-delay adaptation and filtering of scalable videostreams [10]. In conjunction with the QoE controller, whichmodels QoE and informs MAP about the predicted level of theFig. 2. Algorithmic representation of Media Aware Proxyperceived quality, the latter is able to either drop or forwardpackets that carry specific layers of a stream to the receivingvideo users. In details, MAP is a network function based onthe Media-Aware Network Element (MANE) standard [11],[12]. Briefly, MANE can be considered as either a middlebox or an application layer getaway capable of aggregating orthinning RTP streams by selectively dropping packets that havethe less significant impact on the user’s video experience. Assuch, MANE has been proposed as an intermediate systemthat is capable to receive and de-packetise RTP traffic inorder to customise the encapsulated network abstraction layerunits, according to client’s and access network’s requirements.Within the context of the proposed media aware architecture,MAP’s role is twofold. Firstly, it acts as a central point ofdecision in order to overcome networking limitations imposedby firewalls and Network Address Translation (NAT) protocolthat are extensively used in real life networks. Secondly, itreceives and parses RTP streams and customises the streamingaccording to the video client’s requirements and networkconditions, based on the QoE aware decision engine of theQoE controller.In particular, MAP, which is designed to run in Linux kernellevel in order to ensure minimum impact on the end-to-enddelay, acts as a transparent proxy of the mobile client and it isresponsible to parse the packets that are destined for all mobileusers over multiple ports. Each received packet is forwardedin a queue and its header is parsed by an RTP parser processin order to identify the embedded video related information,without changing the header’s fields. The above-mentionedalgorithmic process is shown in Fig. 2.IV. FRAMEWORK EVALUATIONA. Experimental set-upThe proposed optimization framework was evaluated basedon an experimental test-bed which included a core networkand an emulated LTE Enhanced Packet Core (EPC) part, asshown in Fig. 3. Specifically, the core network part consistsof several OpenFlow switches that allow their switch flow3Fig. 3. Experimental test-bedtable to be controlled remotely by the SDN controller. Thescalable video streams were transmitted over the SDN networkwhere different priorities where asigned to each of the flows,according to the proposed algorithm of Fig. 1. For the purposesof this research the test-bed included FloodLight v1.0 [15] asthe SDN controller, OpenvSwitch (OVS) v.2.0.2 [16] for theOpenFlow switches and the OpenFlow 1.3 protocol [17] as thecommunication infrastructure between the SDN controller andthe Openflow switches.Moreover, the developed real-time monitoring and analysisSDN tool is independent of both the platform and the con-troller, while it operates on top of every SDN controller, re-sponsible for providing dynamic QoS path allocation and pathswitching mechanisms to each of the video flows, accordingto pre-defined priorities. This could be achieved by accurateand near real-time traffic measurements across the entireSDN network, along with a number of different techniquesincluding, flow marking, monitoring, manipulation and videothroughput estimation mechanisms. It is worth mentioningthat the proposed application tool provides all the abovefeatures by triggering native OpenFlow 1.3 protocol messagesfrom the SDN controller to the OpenFlow switches, such as“OFP MULTIPART REQ” for accurate traffic measuring and“OFP FLOW MOD” for flow marking and manipulation.Additionally, MAP is located between the core networkand the Packet Data Network (PDN) gateway and managesthe incoming traffic and filters the video streams accordingto the access network conditions. The emulated EPC part ofthe test-bed, comprises of virtual machines according to theEPC topology and support basic mobility management andmonitoring functionalities.B. Video SequencesFor the purposes of this study four common real-worldcaptured stereo video test sequences (“park joy”, “croud run”,“touchdown pass” and “rush field cuts”), were used [13]. Allsequences had spatial resolution of 704×576 pixels at a frameTABLE I. ENCODING AND STREAMING PARAMETERSVideo Sequence Park Crowd Touch RushNo of Frames 1080 framesIntra period 8 framesQP base-enhancement 36-30Frame rate 30 frames per secondResolution 704× 576 pixelsMedia Transmission Unit 1500 bytesrate of 30 frames per second. The first two and the last twosequences were merged together, in order to form longer videosequences. Thus the experiments could last longer, providingadequate time for the evaluating of the proposed optimizationframework. The new sequences have been named as “ParkCrowd” and “Touch Rush”, respectively. The encoding charac-teristics, as well as, the streaming parameters are summarisedin Table IThe H.264/SVC encoding was performed using the encoderprovided by Vanguard Software tool [14] configured to createtwo layers, one base layer and one enhancement layer, usingMedium Grain Scalability, while the group of pictures (GOP)size was 8 frames long. The SVC was the favourable choicefor the particular experiments, as it allows video content tobe delivered over multiple paths according to the SDN pathselection algorithms and the heterogeneous wireless channelswith a manageable bit rate. The particular encoder can nativelycompensate for packet losses up to 5% through a collectionof patent protected error concealment schemes. Each videoframe was encapsulated in a single network abstraction layerunit (NALU), which in turn was encapsulated into a singleReal-Time Transport Protocol (RTP) packet with a maximumpayload size of 1400 bytes, so as to avoid IP fragmenta-tion. The generated video streams are transmitted throughthe network using the UDP/IP protocol stack. Additionally,the Parameter Sets (PS) packets are transmitted to the clientthrough a separate TCP/IP channel in order to avoid any losses.C. ResultsThe video sequences used throughout the experiments have adifferent behaviour in terms of throughput variations, as shownin Fig. 4. The solid line, indicates the goodput generated at theserver for both sequences, while the dashed line represents thewireless medium saturation throughput for the two cases. Sinceboth video sequences have different average and maximumthroughput values, the saturation throughput was set at adifferent value for each. Hence, the resulted packet loss couldbe measured to be the same for both sequences and in therange of 5%.Particular interest has been given to the prioritization of thebase layer. Specifically, the proposed SDN flow control andpath selection mechanism, assigned higher priority to the baselayer, ensuring that the relevant RTP packets would flow overuncongested paths along the SDN network. In parallel, theMAP at the EPC level would selectively drop enhancementlayer packets, in order to guarantee the error free deliveryof the base layer. The impact of both methodologies on the4Fig. 4. Transmission data rate of both video sequences versus the wirelesssaturation throughput (dashed line)Fig. 5. Base layer PLR for “Park Crowd” sequenceFig. 6. Base layer PLR for “Touch Rush” sequencemeasured packet loss of the base layer packets is illustrated inFigs 5 and 6.The performance of the proposed optimization frameworkis compared in terms of perceived video quality, against abaseline scenario where video packets are delivered from theSDN and the access networks as best-effort traffic. Fig. 7and Fig. 8 compare the measured Peak Signal to Noise Ratio(PSNR) per frame, for both cases during the experiment timeperiod. It is evident that the proposed SDN and wirelessaccess optimization scheme achieves higher perceived qualitymeasured in PSNR, than the baseline scenario. The resultsare consistent for the two video sequences. On average, theproposed framework results in higher PSNR by 1.78dB, thanthe non-optimised video delivery. The performance increases inthe case of “Park Crowd”, where the resulted PSNR is 2.34dBhigher than the baseline scenario.The results are consistent and in the case of the NTIAFig. 7. PSNR Score for “Park Crowd” Video SequenceFig. 8. PSNR Score for “Touch Rush” Video SequenceFig. 9. VQM Score for “Park Crowd” Video SequenceVideo Quality Metric (VQM) [18]. Since VQM considers theimpact of the blurring, jerkiness global noise, block and colordistortion to the overall video quality, it correlates better withthe human visual system than PSNR. Hence the use of VQMresults would enhance the validity of the proposed frame-work’s performance evaluation. In fact, the results illustratedin Fig. 9 and Fig. 10, indicate clearly the advantages of theproposed scheme. For both video sequences VQM results aresignificantly better (i.e., lower values than in the case of non-optimised transmission).V. CONCLUSIONSEvidently, a new wave of IP convergence is being fu-eled by real-time multimedia applications. As new networkarchitecture paradigms are constantly evolving, there is anongoing effort by content providers and operators to provideand maintain premium content access with optimized QoS5Fig. 10. VQM Score for “Touch Rush” Video Sequenceand QoE. This paper presented a novel quality-aware videooptimisation scheme designed to couple SDN’s dynamic videoflow adaptation and path switching with a media-aware optimi-sation mechanism located inside the LTE EPC. The proposedsynergy prioritised the base layer video packets and increasedits throughput resulting in a higher than the baseline videodelivery scenario, perceptual video quality. The performanceof the proposed framework was evaluated using the full-referenced objective video quality metrics PSNR and VQM,which indicated the overall better video quality achieved withthe proposed optimisation scheme.ACKNOWLEDGEMENTThis work is part of the Bilateral Greek-Israel DIOGENESproject funded by the National Strategic Reference Framework.REFERENCES[1] I. Politis, L. Dounis, C. Tselios, A. Kordelas, T. Dagiuklas and A.Papadakis “A model of network related QoE for 3D video,” IEEEGlobecom Workshops, Anaheim CA, December 2012.[2] C. Tselios, I. Politis, K. Birkos, T. Dagiuklas and S. Kotsopoulos “Cloudfor Multimedia applications and services over heterogeneous networksensuring QoE,” IEEE CAMAD, Berlin, September 2013.[3] L. Vaquero, L. Rodero-Merino, J. Caceres, & M. Lindner, “A break inthe clouds: towards a cloud definition,” SIGCOMM Comput. Commun.Rev., Vol. 39, pp. 50-55, January 2009.[4] IEEE Internet Computing, Special Issue on Cloud Computing, October2009.[5] https://www.opennetworking.org/images/stories/downloads/white-papers/wp-sdn-newnorm.pdf[6] S. Shenker, M. Casado, T. Koponen, and N. McKeown, “The future ofnetworking, and the past of protocols,” Keynote at Open NetworkingSummit, October 2011.[7] https://www.opennetworking.org/sdn-resources/openflow [Accessed14/03/2015][8] https://www.openstack.org/ [Accessed 12/03/2015][9] http://opennaas.org/ [Accessed 14/03/2015][10] Kordelas A., Politis I., Lykourgiotis A., Dagiuklas T., Kotsopoulos S.,“An media aware platform for real-time stereoscopic video streamingadaptation,” in Communications Workshops (ICC), 2013 IEEE Interna-tional Conference on, pp.687-691, 9-13 June 2013.[11] IETF Draft RFC 3984, “RTP Payload Format for H.264 Video,” draft-ietf-avt-rtp-rfc3984bis-12, Oct., 2010[12] IETF Draft Audio/Video Transport WG, “RTP Payload Format for SVCVideo”, draft-ietf-avt-rtp-svc-27, Feb. 2011.[13] Xiph.Org Foundation https://media.xiph.org/video/derf/[14] Vanguard Software Solutions H.264/SVC SDK, http://www.vanguardsw.com/[15] Floodlight http://www.projectfloodlight.org/floodlight/[16] Open vSwitch (OvS) http://openvswitch.org/[17] Specification, OpenFlow Switch. “Version 1.3.0”, Open NetworkingFoundation (2012).[18] ITU-R Rec. BT.1683, “Objective perceptual video quality measurementtechniques for standard definition digital broadcast television in thepresence of a full reference,” 2004.

End-to-end quality aware optimization for multimedia clouds

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End-to-end quality aware optimization for multimedia clouds

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