Scheduling algorithms for broadcasting media with multiple distortion measures

  • Authors:

  • Carri W. Chan;Nick Bambos;Susie Wee;John Apostolopoulos

  • Affiliations:

  • Department of Electrical Engineering, Stanford University, Stanford, CA;Department of Electrical Engineering and Department of Management Science & Engineering, Stanford University, Stanford, CA;Experience Software Business, Hewlett-Packard, Cupertino, CA;Multimedia Communications and Networking Lab, Hewlett-Packard Laboratories, Palo Alto, CA

  • Venue:
  • IEEE Transactions on Wireless Communications
  • Year:
  • 2009

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Abstract

The growing popularity of multimedia streaming applications brings a growth in diversity of media clients (laptops, PDAs, cellphones). Effectively serving this heterogeneous group of users is highly desirable. Scalable media codecs such as H.264/MPEG-4 SVC help make this adaptation possible. To account for the various capabilities and requests of each user, such as varying spatial or temporal resolutions, Multiple Distortion Measures (MDM) are considered [1]. Rather than consider a homogeneity in users, the MDM framework considers multiple different distortion values for each media packet for each user type. We consider the scenario of simultaneously broadcasting a video stream to multiple users over wireless links. The objective is to design a scheduling algorithm which achieves the highest aggregate Quality-of-Service, measured by distortion and delay, over all different user types. We cast the problem as a stochastic shortest path problem and use Dynamic Programming to find the optimal policy. For statistically static channels, the optimal policy is shown to be of threshold type. For time-varying channels, a quasi-static policy is introduced. Experimental results show that our policy reduces distortion by up to a factor of 2 over conventional approaches which do not consider MDM.