Abstract
This paper proposes an advanced video streaming system based on scalable video coding in order to optimize resource utilization in wireless networks with retransmission mechanisms at radio protocol level. The key component of this system is a packet scheduling algorithm which operates on the different substreams of a main scalable video stream and which is implemented in a so-called media aware network element. The concerned type of transport channel is a dedicated channel subject to parameters (bitrate, loss rate) variations on the long run. Moreover, we propose a combined scalability approach in which common temporal and SNR scalability features can be used jointly with a partitioning of the image into regions of interest. Simulation results show that our approach provides substantial quality gain compared to classical packet transmission methods and they demonstrate how ROI coding combined with SNR scalability allows to improve again the visual quality.
Highlights
Streaming video applications are involved in an increasing number of communication services
In order to do this, we propose an algorithm located in a so-called media aware network element (MANE) which performs a bitstream adaptation between RTP and RLC layers based on an estimation of transport channel conditions
This study proposes a complete framework for scalable and media aware adaptive video streaming over wireless networks
Summary
Streaming video applications are involved in an increasing number of communication services. Packet scheduling algorithms can switch between different versions of the streamed video, encoded with different qualities, instead of pruning the previous set of dependent data units These methods based on rate (congestion)distortion optimized packet scheduling are in theory likely to provide an optimal solution to media aware scheduling problem. In order to do this, we propose an algorithm located in a so-called media aware network element (MANE) which performs a bitstream adaptation between RTP and RLC layers based on an estimation of transport channel conditions. This adaptation is made possible thanks to the splitting of the main scalable stream into different substreams.
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