In-network adaptation of SHVC video in software-defined networks

Software Defined Networks (SDN), when combined with Network Function Virtualization (NFV) represents a paradigm shift in how future networks will behave and be managed. SDN’s are expected to provide the underpinning technologies for future innovations such as 5G mobile networks and the Internet of Everything. The SDN architecture offers features that facilitate an abstracted and centralized global network view in which packet forwarding or dropping decisions are based on application flows. Software Defined Networks facilitate a wide range of network management tasks, including the adaptation of real-time video streams as they traverse the network. SHVC, the scalable extension to the recent H.265 standard is a new video encoding standard that supports ultra-high definition video streams with spatial resolutions of up to 7680×4320 and frame rates of 60fps or more. The massive increase in bandwidth required to deliver these U-HD video streams dwarfs the bandwidth requirements of current high definition (HD) video. Such large bandwidth increases pose very significant challenges for network operators. In this paper we go substantially beyond the limited number of existing implementations and proposals for video streaming in SDN’s all of which have primarily focused on traffic engineering solutions such as load balancing. By implementing and empirically evaluating an SDN enabled Media Adaptation Network Entity (MANE) we provide a valuable empirical insight into the benefits and limitations of SDN enabled video adaptation for real time video applications. The SDN-MANE is the video adaptation component of our Video Quality Assurance Manager (VQAM) SDN control plane application, which also includes an SDN monitoring component to acquire network metrics and a decision making engine using algorithms to determine the optimum adaptation strategy for any real time video application flow given the current network conditions. Our proposed VQAM application has been implemented and evaluated on an SDN allowing us to provide important benchmarks for video streaming over SDN and for SDN control plane latency.

[1]  Sujata Banerjee,et al.  DevoFlow: scaling flow management for high-performance networks , 2011, SIGCOMM 2011.

[2]  James Nightingale,et al.  Optimised transmission of H.264 scalable video streams over multiple paths in mobile networks , 2010, IEEE Transactions on Consumer Electronics.

[3]  Heiko Schwarz,et al.  Overview of the Scalable Video Coding Extension of the H.264/AVC Standard , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Fernando A. Kuipers,et al.  OpenNetMon: Network monitoring in OpenFlow Software-Defined Networks , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[5]  A. Murat Tekalp,et al.  Scalable video streaming over OpenFlow networks: An optimization framework for QoS routing , 2011, 2011 18th IEEE International Conference on Image Processing.

[6]  Jian Yang,et al.  A multicast architecture of SVC streaming over OpenFlow networks , 2014, 2014 IEEE Global Communications Conference.

[7]  Wang Xin Overview of the H.264 / AVC Video Coding Standard , 2003 .

[8]  Ajay Luthra,et al.  Overview of the H.264/AVC video coding standard , 2003, IEEE Trans. Circuits Syst. Video Technol..

[9]  Phuoc Tran-Gia,et al.  SDN-Based Application-Aware Networking on the Example of YouTube Video Streaming , 2013, 2013 Second European Workshop on Software Defined Networks.

[10]  Burak Gorkemli,et al.  A QoS-enabled OpenFlow environment for Scalable Video streaming , 2010, 2010 IEEE Globecom Workshops.

[11]  Jahangir Dadkhah Chimeh 5G Mobile Communications: A mandatory wireless infrastructure for Big data , 2015 .

[12]  Olatunde Awobuluyi,et al.  Periodic Control Update Overheads in OpenFlow-Based Enterprise Networks , 2014, 2014 IEEE 28th International Conference on Advanced Information Networking and Applications.

[13]  Panagiotis Georgopoulos,et al.  Leveraging SDN to provide an in-network QoE measurement framework , 2015, 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[14]  Paul Goransson,et al.  Software Defined Networks: A Comprehensive Approach , 2014 .

[15]  Nick McKeown,et al.  Confused, timid, and unstable: picking a video streaming rate is hard , 2012, Internet Measurement Conference.

[16]  Kuang-Ching Wang,et al.  OpenFlow-based load balancing for wireless mesh infrastructure , 2014, 2014 IEEE 11th Consumer Communications and Networking Conference (CCNC).

[17]  Yashar Ganjali,et al.  HyperFlow: A Distributed Control Plane for OpenFlow , 2010, INM/WREN.

[18]  Daoyun Hu,et al.  Demonstration of OpenFlow-Controlled Network Orchestration for Adaptive SVC Video Manycast , 2015, IEEE Transactions on Multimedia.

[19]  Qi Wang,et al.  QoE-Driven, Energy-Aware Video Adaptation in 5G Networks: The SELFNET Self-Optimisation Use Case , 2016, Int. J. Distributed Sens. Networks.

[20]  James Nightingale,et al.  Scalable HEVC (SHVC)-Based video stream adaptation in wireless networks , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[21]  A. Murat Tekalp,et al.  Video streaming over software defined networks with server load balancing , 2015, 2015 International Conference on Computing, Networking and Communications (ICNC).

[22]  Vyas Sekar,et al.  Understanding the impact of video quality on user engagement , 2011, SIGCOMM.