  In this paper, we present an approach for joint rate allocation and quality
selection for a novel video streaming scheme called streamloading.
Streamloading is a recently developed method for delivering high quality video
without violating copyright enforced restrictions on content access for video
streaming. In regular streaming services, content providers restrict the amount
of viewable video that users can download prior to playback. This approach can
cause inferior user experience due to bandwidth variations, especially in
mobile networks with varying capacity. In streamloading, the video is encoded
using Scalable Video Coding, and users are allowed to pre-fetch enhancement
layers and store them on the device, while base layers are streamed in a near
real-time fashion ensuring that buffering constraints on viewable content are
met.
  We begin by formulating the offline problem of jointly optimizing rate
allocation and quality selection for streamloading in a wireless network. This
motivates our proposed online algorithms for joint scheduling at the base
station and segment quality selection at receivers. The results indicate that
streamloading outperforms state-of-the-art streaming schemes in terms of the
number of additional streams we can admit for a given video quality.
Furthermore, the quality adaptation mechanism of our proposed algorithm
achieves a higher performance than baseline algorithms with no (or limited)
video-centric optimization of the base station's allocation of resources, e.g.,
proportional fairness.
