Recent deep-learning-based video compression models have demonstrated superior performance over traditional codecs. However, few studies have focused on deep learning rate control. In this paper, end-to-end rate control is proposed for deep contextual video compression (DCVC). With the designed two-branch residual-based network, the optimal bit rate ratio is predicted according to the feature correlation of the adjacent frames. Then, the bit rate can be reasonably allocated for every frame by satisfying the temporal feature. To minimize the rate distortion (RD) cost, the optimal λ of the current frame can be obtained from a two-branch regression-based network using the temporal encoded information. The experimental results show that the achievable BD-rate (PSNR) and BD-rate (SSIM) of the proposed algorithm are -0.84% and -0.35%, respectively, with 2.25% rate control accuracy.

In this paper, the quality and transferred data based video bitrate control method for web-conferencing services is proposed, aiming to reduce transferred data by suppressing excessive quality. In web-conferencing services, the video bitrate is generally controlled in accordance with the network conditions (e.g., jitter and packet loss rate) to improve users' quality. However, in such a control, the bitrate is excessively high when the network conditions is sufficiently high (e.g., high throughput and low jitter), which causes an increased transferred data volume. The increased volume of data transferred leads to increased operational costs, such as network costs for service providers. To solve this problem, we developed a method to control the video bitrate of each user to achieve the required quality determined by the service provider. This method is implemented in an actual web-conferencing system and evaluated under various conditions. It was shown that the bitrate could be controlled in accordance with the required quality to reduce the transferred data volume.

This paper extends our previously reported non-orthogonal multiple access (NOMA)-based highly-efficient and low-latency hybrid automatic repeat request (HARQ) method for ultra-reliable low latency communications (URLLC) to the case with inter-base station cooperation. In the proposed method, delay-sensitive URLLC packets are preferentially multiplexed with best-effort enhanced mobile broadband (eMBB) packets in the same channel using superposition coding to reduce the transmission latency of the URLLC packet while alleviating the throughput loss in eMBB. Although data transmission to the URLLC terminal is conducted by multiple base stations based on inter-base station cooperation, the proposed method allocates radio resources to URLLC terminals which include scheduling (bandwidth allocation) and power allocation at each base station independently to achieve the short transmission latency required for URLLC. To avoid excessive radio resource assignment to URLLC terminals due to independent resource assignment at each base station, which may result in throughput degradation in eMBB terminals, we employ an adaptive path-loss-dependent weighting approach in the scheduling-metric calculation. This achieves appropriate radio resource assignment to URLLC terminals while reducing the packet error rate (PER) and transmission delay time thanks to the inter-base station cooperation. We show that the proposed method significantly improves the overall performance of the system that provides simultaneous eMBB and URLLC services.

Hybrid automatic repeat request (HARQ) is an essential technology that efficiently reduces the transmission error rate. However, for ultra-reliable low latency communications (URLLC) in the 5th generation mobile communication systems and beyond, the increase in latency due to retransmission must be minimized in HARQ. In this paper, we propose a highly-efficient low-latency HARQ method built on non-orthogonal multiple access (NOMA) for URLLC while minimizing the performance loss for coexisting services (use cases) such as enhanced mobile broadband (eMBB). The proposed method can be seen as an extension of the conventional link-level non-orthogonal HARQ to the system-level protocol. This mitigates the problems of the conventional link-level non-orthogonal HARQ, which are decoding error under poor channel conditions and an increase in transmission delay due to restrictions in retransmission timing. In the proposed method, delay-sensitive URLLC packets are preferentially multiplexed with best-effort eMBB packets in the same channel using superposition coding to reduce the transmission latency of the URLLC packet while alleviating the throughput loss in eMBB. This is achieved using a weighted channel-aware resource allocator (scheduler). The inter-packet interference multiplexed in the same channel is removed using a successive interference canceller (SIC) at the receiver. Furthermore, the transmission rates for the initial transmission and retransmission are controlled in an appropriate manner for each service in order to deal with decoding errors caused by error in transmission rate control originating from a time varying channel. We show that the proposed method significantly improves the overall performance of a system that simultaneously provides eMBB and URLLC services.

Since HEVC intra rate control has no prior information to rely on for coding, it is a difficult work to obtain the optimal λ for every coding tree unit (CTU). In this paper, a convolutional neural network (CNN) based intra rate control is proposed. Firstly, a CNN with two last output channels is used to predict the key parameters of the CTU R-λ curve. For well training the CNN, a combining loss function is built and the balance factor γ is explored to achieve the minimum loss result. Secondly, the initial CTU λ can be calculated by the predicted results of the CNN and the allocated bit per pixel (bpp). According to the rate distortion optimization (RDO) of a frame, a spatial equation is derived between the CTU λ and the frame λ. Lastly, The CTU clipping function is used to obtain the optimal CTU λ for the intra rate control. The experimental results show that the proposed algorithm improves the intra rate control performance significantly with a good rate control accuracy.

Low Earth Orbit (LEO) satellite networks serve as a powerful complement to the terrestrial networks because of their ability to provide global coverage. In LEO satellite networks, the network is prone to congestion due to several reasons. First, the terrestrial gateways are usually located within a limited region leading to congestion of the nodes near the gateways. Second, routing algorithms that merely adopt shortest paths fail to distribute the traffic uniformly in the network. Finally, the traffic input may exceed the network capacity. Therefore, rate control and load-balancing routing are needed to alleviate network congestion. Moreover, different kinds of traffic have different Quality of Service (QoS) requirements which need to be treated appropriately. In this paper, we investigate joint rate control and load-balancing routing in LEO satellite networks to tackle the problem of network congestion while considering the QoS requirements of different traffic. The joint rate control and routing problem is formulated with the throughput and end-to-end delay requirements of the traffic taken into consideration. Two routing schemes are considered which differ in whether or not different traffic classes can be assigned different paths. For each routing scheme, the joint rate control and routing problem is formulated. A heuristic algorithm based on simulated annealing is proposed to solve the problems. Besides, a snapshot division method is proposed to increase the connectivity of the network and reduce the number of snapshots by merging the links between satellites and gateways. The simulation results show that compared with methods that perform routing and rate control separately, the proposed algorithm improves the overall throughput of the network and provides better QoS guarantees for different traffic classes.

In this paper, we propose a new cross-layer scheme Cooperation between channel Access control and TCP Rate Adaptation (CATRA) aiming to manage TCP flow contention in multi-hop ad hoc networks. CATRA scheme collects useful information from MAC and physical layers to estimate channel utilization of the station. Based on this information, we adjust Contention Window (CW) size to control the contention between stations. It can also achieve fair channel access for fair channel access of each station and the efficient spatial channel usage. Moreover, the fair value of bandwidth allocation for each flow is calculated and sent to the Transport layer. Then, we adjust the sending rate of TCP flow to solve the contention between flows and the throughput of each flow becomes fairer. The performance of CATRA is examined on various multi-hop network topologies by using Network Simulator (NS-2).

Rate control that is required to regulate the bitrate of video coding is critical to time-sensitive video applications used over networks. However, the H.264/AVC standard does not respond to scene changes, and this causes the transmission quality to deteriorate as a scene change occurs. In this work, a scene change is detected by comparing the ratio of the sum of absolute difference (SAD) between two consecutive frames. As the scene change is detected, the proposed method, which is modified from the reference software of H.264/AVC, re-assigns a quantization parameter (QP) value to regulate the bitrate. Because the inter-prediction works poorly for the scene-changed frame, the proposed method estimates its frame complexity based on the content, and further creates another Q-R model to assign QP. The adaptive rate control mechanism presented in this study can quickly respond to the heavy bitrate increment caused by a change of scene. Simulation results show that the proposed method improves the average peak signal noise ratio (PSNR) to approximately 1.1dB, with a smaller buffer size compared with the performance of the reference software JM version 17.2.

In this paper, we investigate the problems of the established congestion solution and then introduce a self-adjustable rate control that supports quality of service assurances over multi-hop wireless mesh networks. This scheme eliminates two phases of the established congestion solution and works on the MAC layer for congestion control. Each node performs rate control by itself so network congestion is eliminated after it independently collects its vector parameters and network status parameters for rate control. It decides its transmission rate based on a predication model which uses a rate function including a congestion risk level and a passing function. We prove that our scheme works efficiently without any negative effects between the network layer and the data link layer. Simulation results show that the proposed scheme is more effective and has better performance than the existing method.

With the approval of IEEE 1901 standard for power line communications (PLC) and the recent Internet-enable home appliances like the IPTV having access to a content-on-demand service through the Internet as AcTVila in Japan, there is no doubt that PLC has taken a great step forward to emerge as the preeminent in-home-network technology. However, existing schemes developed so far have not considered the PLC network connected to an unstable Internet environment (i.e. more realistic situation). In this paper, we investigate the communication performance from the end-user's perspective in networks with large and variable round-trip time (RTT) and with the existence of cross-traffic. Then, we address the problem of unfair bandwidth allocation when multiple and different types of flows coexist and propose a TCP rate control considering the difference in terms of end-to-end delay to solve it. We validate our methodology through simulations, and show that it effectively deals with the throughput unfairness problem under critical communication environment, where multiple flows with different RTTs share the PLC and cross-traffic exists on the path of the Internet.

In our previous work [2], we proposed a new concept of utility functions for rate control in communication networks. Unlike conventional utility-based rate control in which the utility function of each user is defined as a function of its transmitting data rate, in [2], we defined the utility function of each user as a function of not only its transmitting data rate but also it receiving data rate. The former is called a session-level utility function and the latter is called a user-level utility function. The user-level utility function reflects the satisfaction with the service of a user with two-way communication, which consists of transmitting and receiving sessions, better than the session-level utility function, since user's satisfaction depends on not only the satisfaction with its transmitting session but also that for its receiving session. In [2], an algorithm that required each user to know the exact utility function of its correspondent was developed. However, in some cases, this information might not be available due to some reasons such as security and privacy issues, and in such cases, the algorithm developed in [2] cannot be used. Hence, in this paper, we develop a new distributed algorithm that does not require each user to know the utility function of its correspondent. Numerical results show that our new algorithm, which does not require the utility information of the correspondent, converges to the same solution to that with the algorithm that requires the utility information of the correspondent.

An adaptive rate controller (ARC) based on an adaptive neural fuzzy inference system (ANFIS) is designed to autonomously adjust the data rate of a mobile heterogeneous network to adapt to the changing traffic load and the user speed for multimedia call services. The effect of user speed on the handoff rate is considered. Through simulations, it has been demonstrated that the ANFIS-ARC is able to maintain new call blocking probability and handoff failure probability of the mobile heterogeneous network below a prescribed low level over different user speeds and new call origination rates while optimizing the average throughput. It has also been shown that the mobile cognitive wireless network with the proposed CS-ANFIS-ARC protocol can support more traffic load than neural fuzzy call-admission and rate controller (NFCRC) protocol.

We study joint rate control and resource allocation with a packet collision constraint that maximizes the total utility of secondary users in cognitive radio networks. We formulate and decouple the original optimization problem into separable subproblems and then develop an algorithm that converges to optimal rate control and resource allocation. The proposed algorithm can operate on different time-scales to reduce the amortized time complexity.

Emerging video surveillance technologies are based on foreground detection to achieve event detection automatically. Integration foreground detection with a modern multi-camera surveillance system can significantly increase the surveillance efficiency. The foreground detection often leads to high computational load and increases the cost of surveillance system when a mass deployment of end cameras is needed. This paper proposes a DSP-based foreground detection algorithm. Our algorithm incorporates a temporal data correlation predictor (TDCP) which can exhibit the correlation of data and reduce computation based on this correlation. With the DSP-oriented foreground detection, an adaptive frame rate control is developed as a low cost solution for multi-camera surveillance system. The adaptive frame rate control automatically detects the computational load of foreground detection on multiple video sources and adaptively tunes the TDCP to meet the real-time specification. Therefore, no additional hardware cost is required when the number of deployed cameras is increased. Our method has been validated on a demonstration platform. Performance can achieve real-time CIF frame processing for a 16-camera surveillance system by single-DSP chip. Quantitative evaluation demonstrates that our solution provides satisfied detection rate, while significantly reducing the hardware cost.

A structured codebook with various codeword configurations is proposed to overcome the sum capacity limitation in a region with finite number of users. Specifically, based on multi-user MIMO platform with a codebook of multiple orthonormal sets, called as per user unitary rate control (PU2RC), we diversify the codeword configuration of each orthonormal set and expand the corresponding codeword configuration. Numerical experiments with respect to several system parameters, such as user density and received signal to noise ratio, show that the proposed codebook offers throughput gains over the conventional system in a small to moderate number of user region.

We propose a new channel adaptive distributed video coding algorithm, which is adaptive to time-varying available bitrate and packet loss ratio. The proposed method controls the quantization parameter according to channel condition of especially error-prone mobile channel. Simulation shows that the proposed algorithm outperforms the conventional rate-control-only algorithm.

H.264/AVC encoder employs rate control to adaptively adjust quantization parameter (QP) to enable coded video to be transmitted over a constant bit-rate (CBR) channel. In this topic, bit allocation is crucial since it is directly related with actual bit generation and the coding quality. Meanwhile, the rate-distortion-optimization (RDO) based mode-decision technique also affects performance a lot for the strong relation among mode, bits, and quality. This paper presents a multi-stage rate control scheme for R-D optimized H.264/AVC encoders under CBR video transmission. To enhance the precision of the complexity estimation and bit allocation, a frequency-domain parameter named mean-absolute-transform-difference (MATD) is adopted to represent frame and macroblock (MB) residual complexity. Second, the MATD ratio is utilized to enhance the accuracy of frame layer bit prediction. Then, by considering the bit usage status of whole sequence, a measurement combining forward and backward bit analysis is proposed to adjust the Lagrange multiplier λMODE on frame layer to optimize the mode decision for all MBs within the current frame. On the next stage, bits are allocated on MB layer by proposed remaining complexity analysis. Computed QP is further adjusted according to predicted MB texture bits. Simulation results show the PSNR improvement is up to 1.13 dB by using our algorithm, and the stress of output buffer control is also largely released compared with the recommended rate control in H.264/AVC reference software JM13.2.

In this paper, we present an effective overlay real-time video multicast system over the Internet. The proposed system effectively integrates overlay multicast technology and video compression technology. Overlay multicast tree and target bit rate are determined to satisfy the given average delay constraint, and H.263+ rate control is implemented to enhance the human visual perceptual quality over the multicast tree. Finally, experimental results are provided to show the performance of the proposed overlay video multicast system over the Internet.

Currently deployed mobile networks including High Speed Downlink Packet Access (HSDPA) offer only best-effort Quality of Service (QoS). In wireless best effort networks, the bandwidth variation is a critical problem, especially, for mobile devices with small buffers. This is because the bandwidth variation leads to packet losses caused by buffer overflow as well as picture freezing due to high transmission delay or buffer underflow. In this paper, in order to provide seamless video streaming over HSDPA, we propose an efficient real-time video streaming method that consists of the available bandwidth (AB) estimation for the HSDPA network and the transmission rate control to prevent buffer overflows/underflows. In the proposed method, the client estimates the AB and the estimated AB is fed back to the server through real-time transport control protocol (RTCP) packets. Then, the server adaptively adjusts the transmission rate according to the estimated AB and the buffer state obtained from the RTCP feedback information. Experimental results show that the proposed method achieves seamless video streaming over the HSDPA network providing higher video quality and lower transmission delay.

Coding complexity is a crucial parameter in rate control scheme. Traditional measures for coding complexity are based on statistic and estimation. This way may cause the imprecise coding complexity and finally bring inaccurate output bit rate more or less. To resolve this problem, we propose a hypothetical virtual coding complexity to imitate the real coding complexity. Based on the proposed coding complexity measure, a novel rate control algorithm is proposed either. Experimental results and analysis show that the proposed mearsure for coding complexity is effective, and our scheme outperforms the JVT-W042 solution by providing more accurate QP prediction, reducing frame skipping, and improving visual quality.