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Energy-efficient Multicasting of Scalable Video Streams over WiMAX Networks. Somsubhra Sharangi, Ramesh Krishnamurti, Mohamed Hefeeda, Senior Member , IEEE Department of Computer Science, Simon Fraser University, Canada IEEE Transactions on Multimedia, vol. 13, no. 1, Feb. 2011, pp. 102-115.
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Energy-efficient Multicasting of Scalable Video Streams over WiMAX Networks Somsubhra Sharangi, Ramesh Krishnamurti, Mohamed Hefeeda, Senior Member, IEEE Department of Computer Science, Simon Fraser University, Canada IEEE Transactions on Multimedia, vol. 13, no. 1, Feb. 2011, pp. 102-115.
Outline • Introduction • Motivation • Problem • Proposed multicasting algorithm • Substream Selection Algorithm (SSA) • Energy Efficient Substream Allocation (EESA) • Simulation • Conclusion
Introduction • WiMAX supports various network services. • One of these services is the Multicast and Broadcast Service (MBS), which can be used to deliver multimedia traffic to large-scale user communities. • Yota Telecom has recently started a mobile TV service with 25 channels over its 10 Mbps mobile WiMAX network. • UDCast has announced plans for developing broadcast TV service supporting around 50 channels over mobile WiMAX.
Introduction • Mobile Video Multicast/Broadcast • Mobile TV users to increase by 55% by 2015 [VisionGain10] • Competing Technologies • LTE MBMS ⇒ Low Bandwidth • WiMAX Advantage • High Bandwidth • Better Video Quality ⇒ Higher Revenue
Introduction • Multicast/Broadcast Service Data Area in Downlink Frame
Motivation • H.264 Scalable Video Coding • Temporal, spatial and quality scalability • Embedded stream metadata information • Supplementary Enhancement Information (SEI) Message • Video Quality: measurement of video signal peak signal-to-noise ratio (PSNR)
Motivation • Example of Scalable Videos Sub-stream l Stream s
Problem • This paper focuses on optimally utilizing the WiMAX Multicast/Broadcast Service to stream multiple scalable videos to mobile receivers. • Select the optimal subset of layers from each scalable stream • Maximize the average quality of all selected substreams
Network Environment • A number of scalable video streams are available at a WiMAX base station. • Each scalable stream s, 1 s S, has at most L layers. … … q12, qsl: PSNR of substream sl r31, rsl: data rate of substream sl
Network Environment • The average video quality is maximized within a scheduling window. • The Scheduling window has P frames • Each frame can accommodateF amount of data and takes time • Maximum amount of data that can be transmitted within the scheduling window is given as C = PF P frames
Substream Selection Algorithm (SSA) • Let V(s, q) denote the set of substreams from stream 1, …, s • no two substreams are selected from the same stream • total quality of the selected substreams is q. V(s, q)= {sl}={11, 22, 32}, where q=3398+3845+3468
Substream Selection Algorithm (SSA) • Let R(s, q) denote the sum of data rates selected in V(s, q) R(s, q)= 187+824+848, where q=3398+3845+3468
Substream Selection Algorithm (SSA) C=1500 R(1,3398)=187 (total) q=3398 3398 3615 3715 3845 187 380 548 824
Substream Selection Algorithm (SSA) x 7113 C=1500 R(1,3398)=187 (total) q=3398+3715=7113 R(1,7113)=? R(2,7713)=187+548=735 3398 3615 3715 3845 7113 187 380 548 824 735
Substream Selection Algorithm (SSA) • Lower bound: Q0 • Upper bound: 2Q0 =848-466=382 =3468-3294=174 / = 0.445
SimulationSetup • Video Encoding: H.264/SVC format • Channel: 10 MHz • Modulation: 16-QAM ¾ • TDD frame: 5ms • Scheduling Window: 1 second= 200 frames • MBS data area: 50kb • Average bit rate of substreams: 100kbps ~ 2.5 Mbps
Simulation • Running Time • (a) Fixed Window Size at 1s • (b) Fixed number of streams at 20
Simulation • Resource Utilization
Simulation • Energy efficiency of the EESA
Simulation • Effect of Receiver Buffer
Conclusion • This paper proposed energy-efficient multicasting of scalable video streams • Maximize the average video stream quality • Reduce receiver energy consumption T h e E N D Thanks for your attention !