This paper introduces a video application-aware cross-layer framework for joint performance-energy optimization,considering the scenario of multiple users upstreaming real-time Motion JPEG2000 video streams to the acc...This paper introduces a video application-aware cross-layer framework for joint performance-energy optimization,considering the scenario of multiple users upstreaming real-time Motion JPEG2000 video streams to the access point of a WiFi wireless local area network and extends the PHY-MAC run-time cross-layer scheduling strategy that we introduced in (Mangharam et al., 2005; Pollin et al., 2005) to also consider congested network situations where video packets have to be dropped. We show that an optimal solution at PHY-MAC level can be highly suboptimal at application level, and then show that making the cross-layer framework application-aware through a prioritized dropping policy capitalizing on the inherent scalability of Motion JPEG2000 video streams leads to drastic average video quality improvements and inter-user quality variation reductions of as much as 10 dB PSNR, without affecting the overall energy consumption requirements.展开更多
Low energy consumption is one of the main challenges for wireless video transmission on battery limited devices. The energy invested at the lower layers of the protocol stack involved in data communication, such as li...Low energy consumption is one of the main challenges for wireless video transmission on battery limited devices. The energy invested at the lower layers of the protocol stack involved in data communication, such as link and physical layer, represent an important part of the total energy consumption. This communication energy highly depends on the channel conditions and on the transmission data rate. Traditionally, video coding is unaware of varying channel conditions. In this paper, we propose a cross-layer approach in which the rate control mechanism of the video codec becomes channel-aware and steers the instantaneous output rate according to the channel conditions to reduce the communication energy. Our results show that energy savings of up to30% can be obtained with a reduction of barely 0.1 dB on the average video quality. The impact of feedback delays is shown to be small. In addition, this adaptive mechanism has low complexity, which makes it suitable for real-time applications.展开更多
文摘This paper introduces a video application-aware cross-layer framework for joint performance-energy optimization,considering the scenario of multiple users upstreaming real-time Motion JPEG2000 video streams to the access point of a WiFi wireless local area network and extends the PHY-MAC run-time cross-layer scheduling strategy that we introduced in (Mangharam et al., 2005; Pollin et al., 2005) to also consider congested network situations where video packets have to be dropped. We show that an optimal solution at PHY-MAC level can be highly suboptimal at application level, and then show that making the cross-layer framework application-aware through a prioritized dropping policy capitalizing on the inherent scalability of Motion JPEG2000 video streams leads to drastic average video quality improvements and inter-user quality variation reductions of as much as 10 dB PSNR, without affecting the overall energy consumption requirements.
基金Project (No. IST-2004-004042) supported by European Project BETSY (BEing on Time Saves energY)
文摘Low energy consumption is one of the main challenges for wireless video transmission on battery limited devices. The energy invested at the lower layers of the protocol stack involved in data communication, such as link and physical layer, represent an important part of the total energy consumption. This communication energy highly depends on the channel conditions and on the transmission data rate. Traditionally, video coding is unaware of varying channel conditions. In this paper, we propose a cross-layer approach in which the rate control mechanism of the video codec becomes channel-aware and steers the instantaneous output rate according to the channel conditions to reduce the communication energy. Our results show that energy savings of up to30% can be obtained with a reduction of barely 0.1 dB on the average video quality. The impact of feedback delays is shown to be small. In addition, this adaptive mechanism has low complexity, which makes it suitable for real-time applications.
