Optimizing Stored Video Delivery for Wireless Networks: The Value of Knowing the Future

Abstract

This paper considers the design of cross-layer opportunistic transport protocols for stored video over wireless networks with a slow varying (average) capacity. We focus on two key principles: 1) scheduling data transmissions when capacity is high; and 2) exploiting knowledge of future capacity variations. The latter is possible when users’ mobility is known or predictable, for example, users riding on public transportation or using navigation systems. We consider the design of cross-layer transmission schedules, which minimize system utilization (and, thus, possibly transmit/receive energy) while avoiding, if at all possible, rebuffering/delays in several scenarios. For the single-user anticipative case where all future capacity variations are known beforehand, we establish the optimal transmission schedule in a generalized piecewise constant thresholding (GPCT) scheme. For the single-user partially anticipative case where only a finite window of future capacity variations is known, we propose an online greedy fixed horizon control (GFHC). An upper bound on the competitive ratio of GFHC and GPCT is established showing how performance loss depends on the window size, receiver playback buffer, and capacity variability. We also consider the multiuser case where one can exploit both future temporal and multiuser diversity. Finally, we investigate the impact of uncertainty in knowledge of future capacity variations, and propose an offline approach as well as an online algorithm to deal with such uncertainty. Our simulations and evaluation based on a measured wireless capacity trace exhibit robust potential gains for our proposed transmission schemes.