Mengbai Xiao

Power efficient mobile video streaming using HTTP/2 server push

This paper proposes a power efficient video streaming mechanism on mobile devices over cellular networks. We first develop an analytical model to identify and quantify the power inefficiency in mobile video streaming, due to the mismatch between HTTP request schedule and the radio resource control schedule. Based on the analytical model, we develop a low power video streaming mechanism by employing the server push technology available in the HTTP/2 protocol. We implemented the server push-based low power streaming mechanism in an HTTP DASH video streaming prototype involving mobile devices and the 4G/LTE cellular network. Our experiments show significant battery power savings on mobile devices using our server push strategy.

Content-adaptive display power saving in internet mobile streaming

Backlight scaling is a technique proposed to reduce the display panel power consumption by strategically dimming the backlight. However, for Internet streaming to mobile devices, a computationally intensive luminance compensation step must be performed in combination with backlight scaling to maintain the perceived appearance of video frames. This step, if done by the CPU, could easily offset the power savings via backlight dimming. Furthermore, computing the backlight scaling values requires per-frame luminance information, which is typically too energy intensive to compute on mobile devices. In this paper, we propose Content-Adaptive Display (CAD) for Internet mobile streaming. CAD uses the mobile devices GPU rather than the CPU to perform luminance compensation at reduced power consumption. Backlight scaling schedule is computed using a more efficient dynamic programming algorithm than existing work. We implement CAD within an Android app and use a Monsoon power meter to measure the real power consumption. Experiments are conducted on more than 470 randomly selected YouTube videos, and results show that CAD can effectively produce power savings.

OpTile: Toward Optimal Tiling in 360-degree Video Streaming

360-degree videos are encoded for adaptive streaming by first projecting the spherical surface onto two-dimensional frames, then encoding these as standard video segments. During playback of these 360-degree videos, the video player renders the portion of the spherical surface in the direction of the users view. These user viewports typically cover only a small portion of the 360 degree surface, causing much of the downloaded bandwidth to be wasted. Tile-based approaches can reduce the wasted bandwidth by cutting video spatially into motion-constrained rectangles. Streaming logic then only needs to download the tiles necessary to render the viewport seen by the user. Existing tile-based approaches cut 360-degree videos into tiles of fixed sizes. These fixed-size tiling approaches, however, suffer from reduced encoding efficiency. Tiling cuts away portions of the video that can be copied by the encoder from adjacent frames or within the current frame that are needed for effective video compression. In this paper, we propose a scheme called OpTile. This scheme tiles a projected 360-degree segment by first estimating per-tile storage costs, then solving an integer linear program (ILP) to obtain an optimal, potentially non-uniform tiling. The ILP objective considers both content-specific characteristics and empirical distributions over user views of the segments. Using a randomly selected training/testing set split, we show that if a streaming algorithm can perfectly predict the user head orientation, our proposed scheme can save up to 73% of downloaded data compared to the non-tiling scheme and up to 44% compared to the best-performing uniform tiling methods.

GoCAD: GPU-Assisted Online Content-Adaptive Display Power Saving for Mobile Devices in Internet Streaming

During Internet streaming, a significant portion of the battery power is always consumed by the display panel on mobile devices. To reduce the display power consumption, backlight scaling, a scheme that intelligently dims the backlight has been proposed. To maintain perceived video appearance in backlight scaling, a computationally intensive luminance compensation process is required. However, this step, if performed by the CPU as existing schemes suggest, could easily offset the power savings gained from backlight scaling. Furthermore, computing the optimal backlight scaling values requires per-frame luminance information, which is typically too energy intensive for mobile devices to compute. Thus, existing schemes require such information to be available in advance. And such an offline approach makes these schemes impractical. To address these challenges, in this paper, we design and implement GoCAD, a GPU-assisted Online Content-Adaptive Display power saving scheme for mobile devices in Internet streaming sessions. In GoCAD, we employ the mobile devices GPU rather than the CPU to reduce power consumption during the luminance compensation phase. Furthermore, we compute the optimal backlight scaling values for small batches of video frames in an online fashion using a dynamic programming algorithm. Lastly, we make novel use of the widely available video storyboard, a pre-computed set of thumbnails associated with a video, to intelligently decide whether or not to apply our backlight scaling scheme for a given video. For example, when the GPU power consumption would offset the savings from dimming the backlight, no backlight scaling is conducted. To evaluate the performance of GoCAD, we implement a prototype within an Android application and use a Monsoon power monitor to measure the real power consumption. Experiments are conducted on more than 460 randomly selected YouTube videos. Results show that GoCAD can effectively produce power savings without affecting rendered video quality.

Reducing display power consumption for real-time video calls on mobile devices

The display subsystem of a mobile device usually consumes 38%-68% [1] of the total battery power in video streaming. Therefore, a few schemes have been designed to reduce the display power consumption. The basic idea is to dim the backlight level while properly compensating the pixel luminance to maintain image fidelity. The luminance compensation and proper backlight level calculation are computation intensive and demand per-frame luminance information. For these reasons, existing schemes only work for video-on-demand where each frame (and thus the luminance information) is available in advance. In addition, they demand additional computing resource support. Otherwise, if the computation is conducted on the mobile device, the power consumption due to such computation can easily offset the power savings from dimming the backlight. In this work, we set to investigate power saving for real-time video calls on mobile devices. Different from video-on-demand, real-time video calls are highly delay sensitive and the frame luminance information is not known in advance. Moreover, video calls often involve multiple streaming sources from multiple (≥2) participants, making it more difficult. Because there are few background changes and the frame rate is usually small in video calls, we design a Greedy Display Power saving scheme, called LCD-GDP, which utilizes the commonly available GPU on mobile devices without demanding additional support. Our design is implemented on WebRTC, a popular real-time web browser based video call standard. Experiments show that our scheme can save up to 33% power consumption in video calls without affecting the video call quality.

Content-Adaptive Display Power Saving for Internet Video Applications on Mobile Devices

Backlight scaling is a technique proposed to reduce the display panel power consumption by strategically dimming the backlight. However, for mobile video applications, a computationally intensive luminance compensation step must be performed in combination with backlight scaling to maintain the perceived appearance of video frames. This step, if done by the Central Processing Unit (CPU), could easily offset the power savings via backlight dimming. Furthermore, computing the backlight scaling values requires per-frame luminance information, which is typically too energy intensive to compute on mobile devices. In this article, we propose Content-Adaptive Display (CAD) for two typical Internet mobile video applications: video streaming and real-time video communication. CAD uses the mobile device’s Graphics Processing Unit (GPU) rather than the CPU to perform luminance compensation at reduced power consumption. For video streaming where video frames are available in advance, we compute the backlight scaling schedule using a more efficient dynamic programming algorithm than existing work. For real-time video communication where video frames are generated on the fly, we propose a greedy algorithm to determine the backlight scaling at runtime. We implement CAD in one video streaming application and one real-time video call application on the Android platform and use a Monsoon power meter to measure the real power consumption. Experiment results show that CAD can save more than 10% overall power consumption for up to 55.7% videos during video streaming and up to 31.0% overall power consumption in real-time video calls.