Bit-rate allocation for multiple video streams: Dual-frame video coding and competitive equilibrium methods
With the advancement of digital video technology in recent years, there has been an enormous surge in the amount of video data sent across networks. In many cases, a transmission link is shared by more than one video stream. Applications where multiple compressed video streams are transmitted simultaneously through a shared channel include direct broadcast satellite (DBS), cable TV, video-on-demand services, disaster relief response, and video surveillance. Some commercial applications are YouTube and instant video streaming by content providers, such as Netflix, where multiple streams are transmitted simultaneously, and in many cases, these streams share a common transmission channel. Recently, in cognitive radio technology, the secondary or unlicensed users share a pool of bandwidth that is temporarily going unused by the primary or licensed users. In such cases, it has been shown that joint bit-rate allocation schemes for multiple streams can perform better than an equal bit-rate allocation.
In this dissertation, we consider the problem of bit-rate allocation for multiple video users sharing a common transmission channel. We consider two separate objectives for bit-rate allocation among multiple video users: (a) improving the video quality averaged across all the users, and (b) improving the video quality of each individual user, compared to the bit-rate allocation for the users when acting independently.
We use dual-frame video with high-quality Long-Term Reference (LTR) frames, and propose multiplexing methods to reduce the sum of Mean Squared Error (MSE) for all the users. We make several improvements to dual-frame video coding by selecting the location and quality of LTR frames. An adaptive buffer-constrained rate-control algorithm is devised to accommodate the extra bits of the high-quality LTR frames. Multiplexing of video streams was studied under the constraint of a video encoder delay buffer. The high-quality LTR frames are offset in time among different video streams. This provides the benefit of dual-frame video coding with high-quality LTR frames while still fitting under the constraint of an output delay buffer. The multiplexing methods show considerable improvement over conventional rate control when the video streams are encoded independently, and over multiplexing methods proposed previously in the literature.
While the average video quality is improved for multiple video users, such methods often rely on identifying the relative complexity of the video streams. In such methods, not all the videos benefit from the multiplexing process. Typically, the quality of high motion videos is improved at the expense of a reduction in the quality of low motion videos. We use a competitive equilibrium allocation of bit-rate to improve the quality of each individual video stream by finding trades between videos across time. A central controller collects rate-distortion information from each video user and makes a joint bit-rate allocation decision. The proposed method uses information about not only the differing complexity of the different video streams at a given instant of time, but also the differing complexity of each stream over time. Using the competitive equilibrium bit-rate allocation approach for multiple video streams, we show that all the video streams perform better or at least as well as with individual encoding.
The centralized bit-rate allocation methods share the video characteristics and involve high computational complexity. In our pricing-based method, we present an informationally decentralized bit-rate allocation for multiple users where a user only needs to inform his demand to an allocator. Each user separately calculates his bit-rate demand based on his video complexity and bit-rate price, where the bit-rate price is announced by the allocator. The allocator adjusts the bit-rate price based on the bit-rate demanded by the users and the total available bit-rate supply. We show that all users improve their quality by the pricing-based decentralized bit-rate allocation method compared to their allocation when acting individually and the results are comparable to the centralized bit-rate allocation.