We consider the problem of bit-rate allocation for multiple video users sharing a common transmission channel. Previously, overall quality of multiple users was improved by exploiting relative video complexity. Users with high-complexity video benefit at the expense of video quality reduction for other users with simpler videos. The quality of all users can be improved by collectively allocating the bit rate in a centralized fashion which requires sharing video information with a central controller. In this paper, 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 for the next period based on the bit rate demanded by the users and the total available bit-rate supply. Simulation results show that all users improve their quality by the pricing-based decentralized bit-rate allocation method compared with their allocation when acting individually. The results of our proposed method are comparable to the centralized bit-rate allocation.

We consider the problem of simultaneous bitrate allocation for multiple video streams. Current methods for multiplexing video streams often rely on identifying the relative complexity of the video streams to improve the combined overall quality. 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. In our approach, we use a competitive equilibrium allocation of bitrate to improve the quality of all the video streams by finding trades between videos across time. A central controller collects rate-distortion information from each video user and makes a joint bitrate allocation decision. Each user encodes and transmits his video at the allocated bitrate through a shared channel. The proposed method uses information about not only the differing complexity of the video streams at every moment but also the differing complexity of each stream over time. Using the competitive equilibrium bitrate allocation approach for multiple video streams, simulation results show that all the video streams perform better or at least as well as with individual encoding. The results of this research will be useful both for ad hoc networks that employ a cluster head model and for cellular architectures.

We consider the multiplexing problem of transmitting multiple video source streams from a server over a shared channel. We use dual-frame video coding with high-quality Long-Term Reference (LTR) frames and propose multiplexing methods to reduce the sum of mean squared error for all the video streams. This paper makes several improvements to dual-frame video coding. A simple motion activity detection algorithm is used to choose the location of LTR frames as well as the number of bits given to such 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 is studied under the constraint of a video encoder delay buffer. Using H.264/AVC, the results show considerable improvement over baseline schemes such as H.264 rate control when the video streams are encoded individually and over multiplexing methods proposed previously in the literature. The high-quality LTR frames are offset in time among different video streams. This provides the benefits of dual-frame coding with high-quality LTR frames while still fitting under the constraint of an output delay buffer.

We consider rate allocation for multiple video users sharing a constant bitrate channel. Previously, overall quality of multiple users was improved by exploiting relative complexity. Users with high complexity video benefit at the expense of video quality reduction for other users with simpler videos. The quality of all users can be improved by collectively allocating the bitrate which requires sharing video information with a central controller. In this paper, we present an informationally decentralized rate allocation for multiple users where a user only needs to inform its demand to an allocator based on its video complexity and bitrate price. Simulation results show that all users improve their quality by our pricing-based decentralized rate allocation method compared to their allocation when acting individually and the results are comparable to the centralized rate allocation.

We consider the transmission of multiple video source streams over a shared channel from a server. Using the rate-distortion curves and dual-frame video coding with high quality long-term reference (LTR) frames, we propose a method to reduce the sum of mean squared error for all the video streams. A simple motion activity detection algorithm was used to determine the amount of high quality given to the LTR frames. Using H.264/AVC, the results show considerable improvement over a baseline scheme where each video stream is provided with equal bitrate.

In dual-frame video coding, one long-term reference (LTR) and one short-term reference (STR) frames are used for motion estimation and compensation. In previous work, it was shown that the performance of video coding can be improved by pulsing the quality of LTR frames in dual-frame video coding, but this increases the encoder delay buffer size. Also, buffer constrained real-time video transmission requires an efficient rate control algorithm to meet the delay requirement. In this paper, we propose a rate control algorithm for dual- frame video coding under a delay buffer constraint. With the proposed rate control algorithm and motion activity detection for determining the LTR quality, simulation results using H.264/AVC show a significant PSNR improvement over H.264 rate control and other rate control algorithms for dual- frame video coding.

In dual-frame video coding, both encoder and decoder store a short-term reference (STR) and a long-term reference (LTR) frame for motion compensation. In past work, LTR frames at regular intervals were assigned higher quality than the other frames to improve overall video quality. In this letter, we present a method of LTR frame selection using simulated annealing, and we show that PSNR is improved compared to the case of evenly spaced LTR frames. To reduce delay and computational complexity, we consider a constraint on the size of the look-ahead window.

In dual frame video coding, one short-term reference frame and one long-term reference frame are available for motion compensation. In prior research, it was shown that overall video quality was improved by allocating bits unevenly among frames so as to periodically create a high-quality frame that could serve as the long-term reference frame for some time. We extend this work to a cognitive radio scenario where pulses of extra bandwidth can be rented by the second, but rental agreements can be cancelled on short notice if the legacy user of that spectrum returns. With a scalable video coder, this pulse of extra bandwidth can be used to improve the quality of the current frame being encoded, or of the past long term frame, or can be spent on future frames if the encoder has access to them in advance. We compare these various uses to explore the advantages of allocating some of the available bandwidth to past or future frames