Rate Adaptation Algorithm Research Articles

Edge Computing Assisted Joint Quality Adaptation for Mobile Video Streaming

Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on improving the quality of experience (QoE) of clients by running the rate adaptation algorithm on the client side. In a cellular environment, this leads to inefficient resource utilization because of the lack of coordination between the competing clients. In cellular networks, the key challenge for HTTP adaptive streaming (HAS) is to optimize the conflicting video quality objectives. Edge cloud-assisted adaptive streaming presents an opportunity to optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. HAS algorithms select the video quality based on the estimated throughput and playback buffer level. In this paper, we first present a joint throughput estimation method for HAS by taking advantage of mobile edge computing. Next, we present an optimized solution for multi-access edge computing (MEC)-assisted HAS by using edge cloud capabilities. Due to the non-deterministic polynomial-time hardness of the problem, we design a heuristic rate adaptation algorithm to jointly enhance the quality metrics of the competing clients. Our extension simulation results show that the proposed edge cloud-assisted rate adaptation algorithm outperforms the existing strategies under different client-side and server-side settings. Furthermore, we show that the proposed algorithm is promising under slow-moving and fast-moving environments.

QoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning

With the popularity of handheld devices, the development of wireless communication technology and the proliferation of multimedia resources, mobile video has become the main business in LTE networks with explosive traffic demands. How to improve the quality of experience (QoE) of mobile video in the dynamic and complex network environment has become a research focus. Dynamic adaptive streaming over HTTP technology introduces adaptive bitrate (ABR) requests at the client side to improve video QoE and various rate adaptation algorithms are also constantly proposed. In view of the limitations of the existing heuristic or learning-based ABR methods, we propose redirecting enhanced Deep Q-learning toward DASH video QoE (RDQ), a QoE-oriented rate adaptation framework based on enhanced deep Q-learning. First, we establish a chunkwise subjective QoE model and utilize it as the reward function in reinforcement learning so that the strategy can converge toward the direction of maximizing the subjective QoE score. Then, we apply several effective improvements of deep Q-learning to the RDQ agent’s neural network architecture and learning mechanism to achieve faster convergence and higher average reward than other learning-based methods. The proposed RDQ agent has been thoroughly evaluated using trace-based simulation on the real-time LTE network data. For disparate network scenarios and different video contents, the RDQ agent can outperform the existing methods in terms of the QoE score. The breakdown analysis shows that RDQ can suppress the number and the duration of the stalling events to the minimum while maintaining high video bitrate, thus achieving better QoE performance than other methods.

Control-Data Separation Architecture for Dual-Band mmWave Networks: A New Dimension to Spectrum Management

The exponential growth in global mobile data traffic, especially with regards to the massive deployment of devices envisioned for fifth generation (5G) mobile networks, has given impetus to exploring new spectrum opportunities to support the new traffic demands. The millimeter wave (mmWave) frequency band is considered as a potential candidate for alleviating the spectrum scarcity. Moreover, the concept of multi-tier networks has gained popularity, especially for dense network environments. In this paper, we deviate from the conventional multi-tier networks and employ the concept of control-data separation architecture (CDSA), which comprises of a control base station (CBS) overlaying the data base station (DBS). We assume that the CBS operates on the sub-6 GHz single band, while the DBS possesses a dual-band mmWave capability, i.e., 26 GHz unlicensed band, and 60 GHz licensed band. We formulate a multi-objective optimization (MOO) problem, which jointly optimizes conflicting objectives: the spectral efficiency (SE) and the energy efficiency (EE). The unique aspect of this paper includes the analysis of a joint radio resource allocation algorithm based on Lagrangian Dual Decomposition (LDD) and we compare the proposed algorithm with the maximal-rate (maxRx), dynamic sub-carrier allocation (DSA) and joint power and rate adaptation (JPRA) algorithms to show the performance gains achieved by the proposed algorithm.

FastScan: Robust Low-Complexity Rate Adaptation Algorithm for Video Streaming over HTTP

This paper proposes and evaluates a novel algorithm for streaming video over HTTP. The problem is formulated as a non-convex optimization problem which is constrained by the predicted available bandwidth, chunk deadlines, available video rates, and buffer occupancy. The objective is to optimize a QoE metric that maintains a tradeoff between maximizing the playback rate of every chunk and ensuring fairness among different chunks for the minimum re-buffering time. We propose FastScan, a low complexity algorithm that solves the problem. The online adaptations for dynamic bandwidth environments are proposed with imperfect available bandwidth prediction. The results of experiments driven by variable bit rate (VBR) encoded video, video platform system (dash.js), and cellular bandwidth traces of a public dataset reveal the robustness of the online version of FastScan algorithm and demonstrate its significant performance improvement as compared to the considered state-of-the-art video streaming algorithms. For example, on an experiment conducted over 100 real cellular available bandwidth traces of a public dataset that spans different available bandwidth regimes, our proposed algorithm (FastScan) achieves the minimum re-buffering (stall) time and the maximum average playback rate in every single trace as compared to Bola, Festive, BBA, RB, FastMPC, and Pensieve algorithms.

Segment Scheduling Scheme for Efficient Bandwidth Utilization of HTTP Adaptive Streaming in Multipath Environments

HyperText Transfer Protocol (HTTP) adaptive streaming has been receiving attention to provide efficient video streaming services by adaptively adjusting the transmission rate. With increased demand for high-quality video streaming, since mobile devices are now equipped with multiple wireless interfaces connected to different networks (3G/4G or Wi-Fi), one possible solution to provide a better quality of experience (QoE) is to use multipath-based transmission schemes. In conventional HTTP adaptive streaming systems, the segment request policy initially operates in a buffering state and switches to a steady state once the playout buffer reaches a threshold. In the steady state, the client generates an ON/OFF traffic pattern that degrades bandwidth utilization and user QoE in multipath environments. In this paper, we propose a segment scheduling scheme for efficient bandwidth utilization in multipath environments. In our scheme, we first propose a collective segment request policy to improve bandwidth utilization and reducing the frequency of OFF periods. To improve network responsiveness, we present a method for adaptively adjusting the request interval. Subsequently, we propose a segment scheduler to prevent the out-of-order problem in multipath environments. Finally, a rate adaptation algorithm for the proposed request policy is proposed. Through simulation, we prove that our scheme significantly improves bandwidth utilization and increases the average bitrate.

LED chip accurate positioning control based on visual servo using dual rate adaptive fading Kalman filter

In this paper, to improve the positioning accuracy of LED chip, a dual rate adaptive fading Kalman filter algorithm with delay compensation is proposed and applied to the LED chip visual servo positioning system. Firstly, a structure of dual rate Kalman filter is introduced to the visual servo control system, which compensate the visual information delay and realize the accurate time sequential coordination of encoder and visual feedback. Then, considering the inaccuracy of system mathematical model, the adaptive forgetting factor is added to the iterative process of above algorithm, and the impact of accumulated model error on system stability is consequently mitigated. Finally, the experimental results show that the proposed method obviously decreases the positioning errors of LED chip and is robust to inaccuracy and uncertainty of system model parameters.

Enhancing QoE for Mobile Users by Environment-Aware HTTP Adaptive Streaming.

HTTP adaptive streaming (HAS) has become a dominated media streaming paradigm in today’s Internet, which enriches the user’s experience by matching the video quality with the dynamic network conditions. A range of HAS mechanisms have been proposed to enhance the Quality of Experience (QoE). However, existing mechanisms ignore the environmental impact in the QoE evaluation of mobile users, while the popularity of mobile video allows users to watch videos in diversified scenarios. In this paper, we propose an environment-aware HAS scheme that fully concentrates on the different criteria for evaluating video QoE under different environments. Using the advantage of the sensors in mobile phones, the scheme constructs and validates a video QoE model based on environment perception and then designs a model-driven, environment-aware HAS rate adaptation algorithm. We also evaluate the scheme with an environment-aware DASH (Dynamic Adaptive Streaming over HTTP) player in real mobile environments. Compared to the benchmark HAS mechanism, the experimental results demonstrate that our scheme can provide appropriate differentiated rate adaptation for different environments, resulting in a higher QoE.

Schedule Adaptation for Ensuring Reliability in RT-WiFi-Based Networked Embedded Systems

With the ever-growing interests in applying wireless technologies for networked embedded systems to serve as the communication fabric, many real-time wireless technologies have been recently developed to support time-critical sensing and control applications. We proposed in previous work the RT-WiFi protocol that provides real-time high-speed predictable data delivery and enables designs to meet time-critical industrial needs. However, without explicit reliability enforcement mechanisms, our previous RT-WiFi design is either subject to uncontrolled packet loss due to noise and other interferences or may suffer from inefficient communication channel usage. In this article, we explicitly consider interference from both Wi-Fi and non-Wi-Fi based interference sources and propose two sets of effective solutions for reliable data transmissions in RT-WiFi-based networked embedded systems. To improve reliability against general non-Wi-Fi based interference, based on rate adaptation and retransmission techniques, we present an optimal real-time rate adaption algorithm together with a communication link scheduler that has low network management overhead. A novel technique called overbooking is introduced to further improve the schedulability of the communication link scheduler while maintaining the required communication reliability. For Wi-Fi-based interference, we present mechanisms that utilize virtual carrier sensing to provide reliable data transmission while co-existing with regular Wi-Fi networks. We have implemented the proposed algorithms in the RT-WiFi network management framework and demonstrated the system performance with a series of experiments.

Proportionally Fair Resource Allocation in Multirate WLAN<roman>s</roman>

Since IEEE has a standardized 802.11 protocol for wireless local-area networks (WLANs), significant work has been done to develop rate adaptation algorithms. Most of the rate adaptation algorithms proposed till now are heuristic, suboptimal, and are competitive in nature. Even though these algorithms have the advantage of being implemented in distributed fashion, their throughput performance will be low as these schemes may converge to inefficient Nash equilibrium. On the other hand, users may cooperatively choose their rates so that a social optimum can be achieved, but there are no known algorithms that do rate adaptation cooperatively and can still be implemented in a distributed fashion. In this paper, we design a centralized algorithm that achieves a social optimum (by conducting rate adaptation cooperatively) while guaranteeing proportional fairness. We show that it converges in, at most, $N$ iterations and has time complexity $O(N^2)$ , where $N$ is the number of users in the system. Furthermore, we propose a distributed algorithm that also serves the same purpose as the centralized algorithm.

Queueing Theoretic Approach to Playout Buffer Model for HTTP Adaptive Streaming

HTTP-based adaptive streaming (HAS) has recently been widely deployed on the Internet. In the HAS system, a video content is encoded at multiple bitrates and the encoded video content is segmented into small parts of fixed durations. The HAS client requests a video segment and stores it in the playout buffer. The rate adaptation algorithm employed in HAS clients dynamically determines the video bitrate depending on the time-varying bandwidth. Many studies have shown that an efficient rate adaptation algorithm is critical to ensuring quality-of-experience in HAS systems. However, existing algorithms have problems estimating the network bandwidth because bandwidth estimation is performed on the client-side application stack. Without the help of transport layer protocols, it is difficult to achieve accurate bandwidth estimation due to the inherent segment-based transmission of the HAS. In this paper, we propose an alternative approach that utilizes the playout buffer occupancy rather than using bandwidth estimates obtained from the application layer. We start with a queueing analysis of the playout buffer. Then, we present a buffer-aware rate adaptation algorithm that is solely based on the mean buffer occupancy. Our simulation results show that compared to conventional algorithms, the proposed algorithm achieves very smooth video quality while delivering a similar average video bitrate.

LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming

Video streaming today accounts for up to 55\% of mobile traffic. In this paper, we explore streaming videos encoded using Scalable Video Coding scheme (SVC) over highly variable bandwidth conditions such as cellular networks. SVC's unique encoding scheme allows the quality of a video chunk to change incrementally, making it more flexible and adaptive to challenging network conditions compared to other encoding schemes. Our contribution is threefold. First, we formulate the quality decisions of video chunks constrained by the available bandwidth, the playback buffer, and the chunk deadlines as an optimization problem. The objective is to optimize a novel QoE metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches. Second, we develop Layered Bin Packing (LBP) Adaptation Algorithm, a novel algorithm that solves the proposed optimization problem. Moreover, we show that LBP achieves the optimal solution of the proposed optimization problem with linear complexity in the number of video chunks. Third, we propose an online algorithm (online LBP) where several challenges are addressed including handling bandwidth prediction errors, and short prediction duration. Extensive simulations with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement as compared to the state-of-the-art SVC streaming algorithms. The proposed algorithm is also implemented on a TCP/IP emulation test bed with real LTE bandwidth traces, and the emulation confirms the simulation results and validates that the algorithm can be implemented and deployed on today's mobile devices.

High-Speed Bi-Directional Transmission Over Multimode Fiber Link in IM/DD Systems

We demonstrate a 14.5-Tb/s bi-directional transmission over 2.2 km of OM2 fiber using the selective excitation of four mode groups based on multiplane light conversion technology, wavelength division multiplexing, and direct detection. Forty wavelengths per mode group are used; each wavelength is modulated with a discrete multitone scheme with a fast Fourier transform length of 1024 subcarriers. An 88-Gs/s digital-to-analog converter is used to drive an external lithium niobate Mach-Zehnder modulator. The impact of each component in the line is studied experimentally. Moreover, Chow's rate-adaptive algorithm for bit and power allocation is optimized and adapted to the region where the bit error rate (BER) increases. In this paper, we are considering a hard-decision forward error correction (FEC) limit corresponding to a BER of 5 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and assume a 7% overhead. To the best of our knowledge, the achieved bitrate in this paper is the highest throughput transmitted over multimode fibers using direct detection.

Modeling and performance analysis of VI‐CRA: A congestion control algorithm for vehicular networks

SummaryThis study proposes a Vehicle ID‐based CAM Rate Adaptation (VI‐CRA) algorithm for beacon messages in the vehicular network. Foremost, an improved vehicle ID–based analytical model is proposed at the MAC layer of vehicular network. The model weighs the random back‐off number chosen by vehicles participating in the back‐off process, with the vehicle ID incorporated in their respective CAMs. This eventually leads to the selection of a vehicle ID–based random back‐off number, minimizing the probability of collision due to same back‐off number selection. It is worth noting that the improved analytical model outperforms the existing works in terms of average packet delay since only one fourth of the contention window size is used throughout the simulation. To enhance the performance of the analytical model, the paper incorporates a congestion control algorithm, by adapting the rate of CAM broadcast over the control channel. The algorithm is designed considering a wide range of scenarios, ranging from nonsaturated to extremely saturated network (in terms of collision probability) and sparsely distributed to teemed network (in terms of vehicular density). For better analyses of simulation results, the algorithm is applied over different vehicle ID–based back‐off numbers. Simulation results for all the back‐off numbers show that vehicle ID–based CAM rate adaptation algorithm performs better than the traditional fixed CAM rate IEEE 802.11p, even at high vehicular density.

Detecting Bitrate Modulation-Based Covert Voice-Over-IP Communication

Steganography based on bitrate modulation is a new type of information hiding approach for Voice over IP (VoIP), which can naturally resist state-of-the-art steganalysis schemes, due to inducing no modifications on the involved protocols and payloads of VoIP packets. To fill this gap, this letter presents a new corresponding detection scheme. Specifically, for the existing three methods based on bitrate modulation, we first reveal obvious regularities for the change intervals of bitrates in steganographic frame streams, which are totally different from the normal streams. Furthermore, exploiting the statistical properties of change intervals, we design a support-vector-machine-based steganalysis scheme. We evaluate the detection performance of the proposed scheme, respectively, with three state-of-the-art rate adaptation algorithms as the generators of normal VoIP streams. The results demonstrate that the proposed scheme can effectively detect the existing three bitrate modulation-based steganographic methods, even at relatively low covert bandwidths.

AARF-HT: Adaptive Auto Rate Fallback for High-Throughput IEEE 802.11n WLANs

Wireless Local Area Network (WLAN) has been progressing rapidly. The IEEE 802.11n Physical (PHY) layer provides wider channel bandwidth, shorter guard interval, and up to four data streams. Therefore PHY 802.11n has a maximum of 128 data rate options from 6.5 Mbps to 600 Mbps. In addition, Medium Access Control (MAC) has been added Aggregate MAC Protocol Data Unit (AMDPU) scheme. If AMPDU is transmitted with a data rate corresponding to the channel conditions, then the probability AMPDU is received without error becomes increased. MAC determines the data rate used for transmitting AMPDU using a rate adaptation algorithm. Therefore some papers have proposed rate adaptation algorithms based on channel conditions. In this paper we propose a new rate adaptation algorithm that we call Adaptive Auto Rate Fallback for High Throughput (AARF-HT). Our development is done using NS-3 simulator version 3.26. AARF-HT algorithm performance is also tested through a number of simulations extensively. The simulation results show the data rate adaptation function based on the channel width, guard interval and the number of spatial streams in IEEE 802.11n WLAN has functioned well. The test results also show the AARF-HT algorithm resulted in higher throughput compared to the AARF algorithm.

Experimental Evaluation of Large Scale WiFi Multicast Rate Control

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the Multicast Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the rate adaptation response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality.

SABA: segment and buffer aware rate adaptation algorithm for streaming over HTTP

Adaptive streaming allows for dynamic adaptation of the bitrate to varying network conditions, to guarantee the best user experience. Adaptive bitrate algorithms face a significant challenge in correctly estimating the throughput, as the throughput varies widely over time. The current throughput estimation methods cannot distinguish between throughput fluctuations of different amplitude and frequency. In this paper, we propose a throughput estimation method that accurately estimates the throughput based on previous throughput samples. It is robust to short term and small fluctuations, and sensitive to large fluctuations in throughput. Furthermore, we propose a rate adaptive algorithm for video on demand (VoD) services that selects the quality of the video based on the estimated throughput and playback buffer occupancy. The objective of the rate adaptive algorithms is to guarantee high video quality to improve user experience. The proposed algorithm dynamically adjusts the quality level of the video stream. The proposed method selects high quality video segments, while minimizing the risk of playback interruption. Furthermore, the proposed method minimizes the frequency of video rate changes. We show that the algorithm smoothly switches the video rate to improve user experience. Furthermore, we determine that it efficiently utilizes network resources to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that variations in the playback buffer size and segment duration do not affect the performance of the proposed algorithm.

A Practical Evaluation of Rate Adaptation Algorithms in HTTP-based Adaptive Streaming

The HTTP-based Adaptive Streaming (HAS) techniques are widely used in Internet video streaming services, including YouTube and Netflix. The Dynamic Adaptive Streaming over HTTP (DASH) is the latest international standard that facilitates the interoperability of different HAS techniques of various vendors. DASH specification defines the media presentation description (MPD), which describes a list of available content, URL addresses, and the segment format. The rate adaptation algorithms, however, are not part of the standard, and the details of the algorithms are left to vendors. As a result, there are many different algorithms adopted in both commercial and open source players while the detailed algorithms and their performance are barely understood. In this paper, we investigate the detailed operations of the different players by code level analysis and through reverse engineering. Specifically, we present the pseudo codes of 3 open source players and devise a method to obtain the detailed operation information, e.g., bitrate and buffer amount, of popular streaming players whose source codes are not publicly available. We conduct extensive experiments on our testbed and provide suggestions based on the behaviors of these players, including the repeated over-estimation of the available bandwidth, unfair bitrate selection when multiple players compete for the bandwidth, and insensitivity of Quick UDP Internet Connections (QUIC) protocol to the varying network bandwidth.

A Bandwidth Variation Pattern-Differentiated Rate Adaptation for HTTP Adaptive Streaming Over an LTE Cellular Network

Currently, HTTP adaptive streaming (HAS) is the state-of-the-art technology for mobile video streaming. The rate adaptation of HAS has been designed to make a trade-off between two contrasting requirements, i.e., enhancing the quality of a video, and reducing the probability of video freezes, by adaptively switching between different video bitrates during a video playback session. This process becomes more challenging when moving onto an long term evolution (LTE) cellular network due to the unstable nature of the wireless channel. In this paper, we propose the bandwidth variation pattern-differentiated rate adaptation (BVPDRA) algorithm for LTE cellular networks. Unlike prior works, BVPDRA does not strike a balance between the stableness and responsiveness of bitrate switching in the case of bandwidth capacity variations. BVPDRA differentiates between bandwidth variation patterns of the LTE cellular network as either constant bandwidth fluctuations or instantaneous bandwidth hopping. Accordingly, BVPDRA operates with a dual character: for the constant bandwidth fluctuations, BVPDRA performs smoothed bandwidth prediction and conservative rate switching to minimize video quality version oscillations; for the instantaneous bandwidth hopping, BVPDRA performs positive bandwidth prediction and aggressive rate switching to maximize the bandwidth utilization and minimize the risk of playback stalling. We empirically evaluate the performance of BVPDRA on an LTE cellular network testbed. The results demonstrate that BVPDRA achieves a higher average bitrate, and lower rebuffering ratio with a reduced bitrate switching frequency.

Adaptive Video with SCReAM over LTE for Remote‐Operated Working Machines

Remote operation is a step toward the automation of mobile working machines. Safe and efficient teleremote operation requires good‐quality video feedback. Varying radio conditions make it desirable to adapt the video sending rate of cameras to make the best use of the wireless capacity. The adaptation should be able to prioritize camera feeds in different directions depending on motion, ongoing tasks, and safety concerns. Self‐Clocked Rate Adaptation for Multimedia (SCReAM) provides a rate adaptation algorithm for these needs. SCReAM can control the compression used for multiple video streams using differentiating priorities and thereby provide sufficient congestion control to achieve both low latency and high video throughput. We present results from the testing of prioritized adaptation of four video streams with SCReAM over LTE and discuss how such adaptation can be useful for the teleremote operation of working machines.