Abstract
Mobile video chatting has emerged as an important Internet multimedia application that greatly enriches interpersonal communications. Mobile power efficiency is crucial to the service quality and time of video chatting on battery-limited smartphones. However, the power characteristics of the video coding and data communication are highly complex due to the time-varying network conditions and dynamic mobile energy features. This incurs crucial challenges to maintaining the low power dissipation of mobile chatting application while streaming satisfactory-quality videos. To address these challenges, this paper presents a joinT cOding-tranSmission Optimization (TOSO) protocol at application layer that performs machine learning based adaptation of the video bit rate and FEC (Forward Error Correction) coding parameters. By taking advantage of analytical and empirical models characterizing the quality-power relationship, TOSO is able to maximize video quality subject to a specified upper bound of power consumption in mobile chat application. This distinguishing feature prevents the video chat from draining battery too quickly. Moreover, it allows the smartphone operating system or the mobile user to define a desired video chat duration given the remaining battery, avoiding unpleasant conversation disruption due to battery depletion. Extensive experiments based on the Linphone platform and Exata network emulator show that TOSO outperforms baseline approaches by 29.3 percent in power conservation while achieving the same video quality level.
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