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 / siyxiang@ece.uvic.ca
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/4485 |
| Date | 12 March 2013 |
| Creators | Xiang, Siyuan |
| Contributors | Cai, Lin |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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