Two recent and conflicting trends in internet applications are video traffic becoming dominant and usage-based pricing becoming prevalent motivates the discussion in this chapter. It gives a brief background on existing video adaptation techniques and streaming protocols. As a potential solution to the problem of video consumption under capped data plans, the QAVA (quota aware video adaptation) system is proposed. The architecture of QAVA comprises three different modules; each one is responsible for a specific function. The modules work together to enable QAVA to optimize across the three performance goals. The chapter describes the video request, utility, and cost model and then formulate the bitrate selection problem. It also describes the functionality of the user profiler (UP) and video profiler (VP) modules and different patterns in user behavior and tastes and then proposes the UP algorithm.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- User profiler (UP)
- Video profiler (VP)