Scalable video transmission over wireless networks

• Main Author: Xiang, Siyuan
• Other Authors: Cai, Lin
• Language: English; en
• Published: 2013
• Subjects: Multimedia Communication; Video Streaming; Dynamic Adaptive Streaming over HTTP; Compressive Sensing
• Online Access: http://hdl.handle.net/1828/4485

With the increasing demand of video applications in wireless networks, how to better support video transmission over wireless networks has drawn much attention to the research community. Time-varying and error-prone nature of wireless channel makes video transmission in wireless networks a challenging task to provide the users with satisfactory watching experience. For different video applications, we choose different video coding techniques accordingly. E.g., for Internet video streaming, we choose standardized H.264 video codec; for video transmission in sensor networks or multicast, we choose simple and energy-conserving video coding technique based on compressive sensing. Thus, the challenges for different video transmission applications are different. Therefore, This dissertation tackles video transmission problem in three different applications. First, for dynamic adaptive streaming over HTTP (DASH), we investigate the streaming strategy. Specifically, we focus on the rate adaptation algorithm for streaming scalable video (H.264/SVC) in wireless networks. We model the rate adaptation problem as a Markov Decision Process (MDP), aiming to find an optimal streaming strategy in terms of user-perceived quality of experience (QoE) such as playback interruption, average playback quality and playback smoothness. We then obtain the optimal MDP solution using dynamic programming. However, the optimal solution requires the knowledge of the available bandwidth statistics and has a large number of states, which makes it difficult to obtain the optimal solution in real time. Therefore, we further propose an online algorithm which integrates the learning and planning process. The proposed online algorithm collects bandwidth statistics and makes streaming decisions in real time. A reward parameter has been defined in our proposed streaming strategy, which can be adjusted to make a good trade-off between the average playback quality and playback smoothness.We also use a simple testbed to validate our proposed algorithm. Second, for video transmission in wireless sensor networks, we consider a wireless sensor node monitoring the environment and it is equipped with a compressive-sensing based, single-pixel image camera and other sensors such as temperature and humidity sensors. The wireless node needs to send the data out in a timely and energy efficient way. This transmission control problem is challenging in that we need to jointly consider perceived video quality, quality variation, power consumption and transmission delay requirements, and the wireless channel uncertainty. We address the above issues by first building a rate-distortion model for compressive sensing video. Then we formulate the deterministic and stochastic optimization problems and design the transmission control algorithm which jointly performs rate control, scheduling and power control. Third, we propose a low-complex, scalable video coding architecture based on compressive sensing (SVCCS) for wireless unicast and multicast transmissions. SVCCS achieves good scalability, error resilience and coding efficiency. SVCCS encoded bitstream is divided into base and enhancement layers. The layered structure provides quality and temporal scalability. While in the enhancement layer, the CS measurements provide fine granular quality scalability. We also investigate the rate allocation problem for multicasting SVCCS encoded bitstream to a group of receivers with heterogeneous channel conditions. Specifically, we study how to allocate rate between the base and enhancement layer to improve the overall perceived video quality for all the receivers. === Graduate === 0984 === [e-mail]