Addressing computational and networking constraints to enable video streaming from wireless appliances

Abstract

Enabling real-time video streaming from a wireless appliance requires compute intensive video compression to be performed in real-time on the appliance before transmitting the data upstream. However, the tasks of real-time video encoding and streaming from the wireless appliances are challenging due to a) limited computational and battery resources, and b) limited and time-varying network bandwidth availability. In this paper, we present a technique for enabling real-time video compression and transmission from wireless appliances based on run-time video adaptation. We present an adaptation engine for dynamic selection of video compression parameters such that both the computational and the network bandwidth constraints are satisfied, while maximizing the end user's viewing quality. The algorithm is based on the analysis of the effect of different video compression parameters on computational and network resource usage, and the video quality. Since our approach is based on judicious selection of video compression parameters and does not require changes to the compression algorithm itself, it is applicable to a wide range of video compression standards. We have also developed an iPAQ-based end-to-end video streaming system to evaluate our approach. Experiments conducted on this test-bed indicate that our proposed technique achieves significant improvements in overall video quality under computation (up to 4/spl times/) and network bandwidth (/spl sim/3dB) constraints. We also show significant improvements in the energy efficiency as a result of adaptation.