Internet Video Streaming and Cloud-based Multimedia Applications. Outline

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1 Internet Video Streaming and Cloud-based Multimedia Applications Yifeng He, Ling Guan, 1 Outline Internet video streaming Overview Video coding Approaches for video streaming over Internet Cloud-based multimedia applications Overview Virtual Machine (VM) Allocation Workload scheduling 2 1

2 Overview of video streaming Video streaming is a real time application Live video streaming E.g., live broadcast of Olympic games Pre encoded video streaming E.g., YouTube 3 Overview of video streaming Objective quality evaluation Peak Signal to Noise Ratio (PSNR) Mean Squared Error (MSE) is distortion Encoding distortion Caused by quantization in video encoding Transmission distortion Caused by packet loss rate (PLR)» Congestion loss» Transmission errors» Excessive delay 4 2

3 Overview of video streaming Video streaming system Sender Camcorder Capture, and encode the video Channel Wired or wireless channel Receiver Decode, and playback the video Sender Encoding distortion Channel Transmission distortion Receiver 5 Overview of video streaming Original YUV before encoding Reconstructed YUV after encoding, transmission, and decoding 6 3

4 Overview of video streaming Protocol stack Sender Compression domain Receiver Encoder Video Decoder Video Video Transport domain Video Channel 7 Overview of video streaming RTP (Real Time Transport Protocol): carries the video payload Payload type identification (e.g., video or audio) Sequence numbering Time stamping RTCP (Real Time Control Protocol): provides feedback on the quality of the packet delivery RTCP monitors the quality of delivery, such as packet count, packet loss, delay. 8 4

5 Overview of video streaming Objective: Improve the received video quality Reduce the encoding distortion Rate distortion tradeoff Error resilience Reduce the transmission distortion Quality of Service (QoS) mechanism for video streaming, to reduce the delay, packet loss. 9 Video coding Input raw video (Foreman CIF 420: Video coder bit rate=36,495,360 bps) Input raw video Resolution Color space YCbCr, (also called YUV) Luma: Y (brightness) Chroma: Cb, Cr (colors) Output compressed bit stream, bit rate = 1,000,000 bps Sampling format 4:2:0 10 5

6 Video Frame Coding Techniques I P P P P P P I B B P B B P 11 Approaches for video streaming over Internet Unicast-based techniques Centralized approach Proxy approach Content delivery network (CDN) approach Multicast-based techniques Network-layer multicast Application-layer multicast (tree-based peer-to-peer live streaming systems) 12 6

7 Approaches for video transmission over Internet Unicast-based techniques Centralized approach Centralized approach 13 Approaches for video transmission over Internet Unicast-based techniques Proxy approach Proxy approach 14 7

8 Approaches for video transmission over Internet Unicast-based techniques Content delivery network (CDN) approach Content Delivery Network (CDN) approach 15 Approaches for video transmission over Internet Multicast-based techniques Network-layer multicast Network-layer multicast 16 8

9 Approaches for video transmission over Internet Apply P2P technology into video streaming applications P2P technology is a promising approach to build distributed and scalable network applications P2P network systems P2P file-sharing systems» For example, BitTorrent P2P streaming systems» P2P live streaming systems For example, PPLive» P2P Video-on-Demand (VoD) systems Different between P2P file-sharing and P2P streaming» P2P file sharing: play after downloading the whole file» P2P streaming: play while the video file is being downloaded Different between P2P live streaming and P2P VoD» P2P live streaming: The video playbacks on all users are synchronized» P2P VoD: The playbacks of the same video on different users are not synchronized 17 Approaches for video transmission over Internet The exchanges of the blocks between two peers in different P2P systems: (a) P2P filesharing systems, (b) P2P live streaming systems, and (c) P2P VoD systems 18 9

10 Approaches for video transmission over Internet Multicast-based techniques Application-layer multicast (tree-based peer-to-peer streaming systems) Tree construction Internal node Leaf node odrawbacks 1. Departure of a peer causes the isolation of all of its descendants from the video source 2. All the leaf nodes do not contribute their uploading bandwidths Single-tree based P2P live streaming system 19 Approaches for video transmission over Internet Mesh-Based P2P Live Streaming Systems Mesh based P2P live streaming systems 20 10

11 Outline Cloud-based multimedia applications Overview Virtual Machine (VM) Allocation Workload scheduling 21 Cloud computing Overview Enable convenient, on demand network access to a shared pool of configurable computing resources (e.g., virtual machine (VM), storage) Cloud computing models Infrastructure as a Service (IaaS) Examples: Amazon EC2 Platform as a Service (PaaS) Example: Google App Engine Software as a Service (SaaS) Example: Gmail 22 11

12 Overview Cloud based multimedia application Users: Send request to MSP and get result Multimedia Service Provider (MSP) Deploy multimedia services on VMs leased from cloud provider Offer multimedia services to users Cloud computing provider Provide VMs Illustration of cloud-based multimedia service 23 VM allocation Virtual Machines (VMs) Resources in cloud are managed as VMs Different configurations have different service rates and different price rates Pricing Strategies On Demand Pricing Strategy No long term commitments Add more VMs on demand High hourly rate Reservation Pricing Strategy A low, one time, upfront payment for a VM instance Reserve it for a one or three year term Significantly lower hourly rate 24 12

13 VM allocation The SaaS architecture for multimedia application providers 25 VM allocation VM allocation problem To optimize virtual machines allocation for each application to achieve the minimal resource cost for multimedia application providers, subject to the round trip time (RTT) requirements for all users. Proposed scheme: Optimize required VMs for each kind of application Use Round Trip Time (RTT) as QoS measurement Model the computation process as M M/M/1 queuing systems 26 13

14 Problem formulation VM allocation Reserved VMs cost On demand VMs cost Constraint on round trip time Constraint on stable queuing Constraint on reserved VMs amount 27 Workload scheduling Workload scheduling User level workload scheduling The requests for one application are distributed to different VMs for workload balancing Task level workload scheduling Each application is decomposed into multiple tasks The goal of task level scheduling is to assign tasks to VMs so that the total execution time can be minimized 28 14

15 Workload scheduling User level workload scheduling Problem statement To optimally schedule users requests to different classes of VMs such that the response time can be minimized. Response Time Minimization Problem Mean response time Subject to Resource constraints; All requests have to be served 29 Workload scheduling User level workload scheduling The workload scheduling model for multimedia cloud. scheduler i 30 15

16 Workload scheduling User level workload scheduling Problem formulation Analytical solution 31 Workload scheduling Task level workload scheduling A directed acyclic graph (DAG) is introduced to represent the precedence constraints among tasks An illustration of video retrieval framework The DAG of the video retrieval framework 32 16

17 Workload scheduling Task level workload scheduling Problem statement To optimally schedule tasks to different VMs such that the total execution time of the application is minimized, subject to the precedence constraints and the cost constraint. 33 Workload scheduling Task level workload scheduling Problem formulation In a mixed structure, some tasks can be processed in parallel, while some tasks have to be processed serially. The DAG of mixed structure has multiple paths from the source to the sink. The total execution time is determined by the time taken by the longest path. Objective: total execution time. Every task should be assigned to one VM instance for execution 34 17

18 References Internet video streaming D. Wu, Y. T. Hou, W. Zhu, Y. -Q. Zhang, and J. M. Peha, Stream video over the Internet: approach and directions, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, No. 3, pp , Mar T. Wiegand, G. Sullivan, G. Bjøntegaard, and A. Luthra, Overview of the H.264/AVC Video Coding Standard, IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, n0.7, Y. He and L. Guan, Peer-to-peer streaming systems, in Intelligent Multimedia Communication: Techniques and Applications. Changwen Chen, Zhu Li, and Shiguo Lian (editors), Springer-Verlag Berlin Heidelberg, SCI 280, pp , Cloud-based multimedia applications X. Nan, Y. He, and L. Guan, Optimal allocation of virtual machines for cloud-based multimedia applications, in Proc. of IEEE International Workshop on Multimedia Signal Processing (MMSP), pp , Banff, Canada, Sep (VM allocation) X. Nan, Y. He, and L. Guan, Optimization of workload scheduling for multimedia cloud computing, in Proc. of IEEE International Symposium on Circuits and Systems (ISCAS), pp , Beijing, China, May (User-level workload scheduling) X. Nan, Y. He, and L. Guan, Optimal task-level scheduling for cloud based multimedia applications, in Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp , Vancouver, Canada, May (Task-level workload scheduling) 35 18