Adaptive Bit Research Articles

Energy-Efficient Proactive Caching for Adaptive Video Streaming via Data-Driven Optimization

Proactive caching in mobile-edge computing (MEC) networks is promising to handle the ever-increasing demand for wireless video services, and transcoding at MEC servers further improves the flexibility of video content delivery. However, how to effectively conduct caching for adaptive bitrate streaming poses great challenges due to the uncertainty of user preferences. The caching decisions also have a profound impact on the system energy efficiency since they may change the video delivery modes. In this article, by integrating caching, transcoding, and backhaul retrieving in a MEC-enabled adaptive streaming system, we propose a holistic solution to jointly determine the caching of bitrate-aware files and the scheduling of video requests in an energy-efficient manner. Specifically, we leverage a data-driven approach to characterize the uncertainty of real request arrivals. Based on the uncertainty model, we formulate a data-driven risk-averse optimization to derive a robust strategy for caching and delivery scheduling, which is a two-stage stochastic mixed-integer programming (SMIP) with the goal of minimizing the total expected energy consumption. We also develop feasible solutions and conduct extensive simulations on real-world data sets. The results validate the effectiveness of the proposed scheme in both the energy efficiency and the cache hit ratio.

An Efficient QoE-Aware HTTP Adaptive Streaming over Software Defined Networking

Due to the increase in video streaming traffic over the Internet, more innovative methods are in demand for improving both Quality of Experience (QoE) of users and Quality of Service (QoS) of providers. In recent years, HTTP Adaptive Streaming (HAS) has received significant attention from both industry and academia based on its impacts on the enhancement of media streaming services. However, HAS-alone cannot guarantee a seamless viewing experience, since this highly relies on the Network Operators’ infrastructure and evolving network conditions. Along with the development of future Internet infrastructure, Software-Defined Networking (SDN) has been researched and newly implemented as a promising solution in improving services of different Internet layers. In this paper, we present a novel architecture incorporating bitrate adaptation and dynamic route allocation. At the client side, adaptation logic of VBR videos streaming is built based on the MPEG-DASH standard. On the network side, an SDN controller is implemented with several routing strategies on top of the OpenFlow protocol. Our experimental results show that the proposed solution enhances at least 38% up to 185% in term of average bitrate in comparison with some existing solutions as well as achieves better viewing experience than the traditional Internet.

Does QUIC Suit Well With Modern Adaptive Bitrate Streaming Techniques?

This letter provides a thorough analysis of various recent adaptive bitrate streaming (ABR) techniques over Google’s QUIC protocol. More specifically, we focus on the recent developments of the ABR techniques using control theoretic and reinforcement learning based approaches, and compare their performances over HTTP/TCP and HTTP/QUIC in terms of a wide range of video quality of experience (QoE) parameters. We observe that these ABR techniques are more compatible with HTTP/TCP compared to HTTP/QUIC, and the features of QUIC demands for new ABR technique which can balance the three fundamental QoE parameters – bitrate, smoothness and rebuffering during a video playback.

New objective QoE models for evaluating ABR algorithms in DASH

As users become more demanding with regards to the consumption of multimedia content, the importance of measuring their level of satisfaction is growing. The difficulty in terms of time and resources for assessing the Quality of Experience (QoE) has popularized the use of objective QoE models, which try to emulate human behavior regarding the playback of multimedia streaming. Some objective QoE models existing in the literature are based on the bitrate. However, the PSNR (Peak Signal-to-Noise Ratio) or VMAF (Video Multimethod Assessment Fusion) have been proved to be metrics with a closer relationship with the QoE than the bitrate. This paper proposes three new models to measure the QoE analytically in DASH (Dynamic Adaptive Streaming over HTTP) video services. The first is based on the bitrate of the displayed video segments, whereas the second and the third are based on the PSNR and VMAF of each video segment, respectively. The proposed models are compared to the ITU-T standard P.1203 as well as the bitrate-based QoE model proposed by Yin et al. Moreover, the paper presents a subjective study, which confirms the validity of the proposed models. The models are validated by using different DASH adaptation algorithms. In this sense, this paper also presents a DASH ABR (Adaptive Bitrate Streaming) algorithm called Look Ahead, which takes into account the inherent bitrate variability of the video encoding process in order to calculate, in real time, the appropriate quality level that minimizes the number of stalls during the playback.

Evaluation of Shared Resource Allocation Using SAND for ABR Streaming

Adaptive bitrate media streaming clients adjust the quality of media content depending on the current network conditions. The shared resource allocation (SRA) feature defined in MPEG-SAND (server and network assisted DASH) allows servers to allocate bandwidth to streaming clients. This enables coordination and prioritization of clients that are connected to the same network bottleneck (e.g., to maximize the number of clients that can play back a stream fluently). In this article, we evaluate different bandwidth limitation strategies and analyze the effects on the clients. For this purpose, a testbed using multiple Raspberry Pis was created. The results show that in various scenarios, SRA improves the fairness and the QoE of streaming sessions. Solely allocating a maximum quality level to the client is not sufficient in some cases. Therefore, additional means, such as limiting bandwidth on the client or traffic shaping with software-defined networking for SRA, are evaluated.

Power-Constrained Quality Optimization for Mobile Video Chatting With Coding-Transmission Adaptation

Mobile video chatting has emerged as an important Internet multimedia application that greatly enriches interpersonal communications. Mobile power efficiency is crucial to the service quality and time of video chatting on battery-limited smartphones. However, the power characteristics of the video coding and data communication are highly complex due to the time-varying network conditions and dynamic mobile energy features. This incurs crucial challenges to maintaining the low power dissipation of mobile chatting application while streaming satisfactory-quality videos. To address these challenges, this paper presents a joinT cOding-tranSmission Optimization (TOSO) protocol at application layer that performs machine learning based adaptation of the video bit rate and FEC (Forward Error Correction) coding parameters. By taking advantage of analytical and empirical models characterizing the quality-power relationship, TOSO is able to maximize video quality subject to a specified upper bound of power consumption in mobile chat application. This distinguishing feature prevents the video chat from draining battery too quickly. Moreover, it allows the smartphone operating system or the mobile user to define a desired video chat duration given the remaining battery, avoiding unpleasant conversation disruption due to battery depletion. Extensive experiments based on the Linphone platform and Exata network emulator show that TOSO outperforms baseline approaches by 29.3 percent in power conservation while achieving the same video quality level.

Adaptive bit and power allocation for dual mode index modulation based OFDM system

Orthogonal frequency division multiplexing (OFDM) along with index modulation (IM) is a promising technique for wide applications that can enhance the spectral efficiency (SE) and simultaneously able to mitigate the harmful effects of dispersive wireless channels. The IM technique utilizes an additional dimension to transmit information in terms of determining whether the OFDM subcarrier is active or inactive. In OFDM-IM system, only active subcarriers carry the modulated data symbol while inactive subcarriers remain empty. The concept of dual-mode (DM) OFDM-IM has been proposed to increase the SE with data transmission through inactive subcarriers employing different constellation alphabets with the same modulation order. In DM-OFDM-IM, all the symbols are modulated with a fixed modulation scheme which provides a constant SE over all the channel conditions. In adaptive DM-OFDM-IM, the modulation order for each subblock of OFDM subcarriers is decided according to the instantaneous received signal to noise ratio (SNR) to improve the bit error rate (BER) performance for enhanced SE. In this work, an idea of simultaneous adaptive bit and power loading (BPL) in DM-OFDM-IM system has been provided in which the information bits and power are allocated for each subblock satisfying both target BER of the system and the total transmission power constraints. The numerical results illustrate that adaptive BPL based DM-OFDM-IM system provides a significant gain in SE for the same BER performance as compared to the conventional DM-OFDM-IM system.

Adaptive Bitrate Video Streaming in Non-Orthogonal Multiple Access Networks

Facing the ever-increasing demand for high quality video streaming and the dramatic variations of wireless network environment, adaptive bitrate video streaming (ABS) emerges as a prominent video quality adaptation technique in improving users’ quality of experience (QoE). Reinforcement learning (RL)-based ABS and network-assisted ABS can improve users’ QoE through resource utilization efficiency enhancement. However, limited network capacity is still the bottleneck of QoE improvement. Considering the potential capacity gain of non-orthogonal multiple access (NOMA), we study network-assisted ABS in NOMA networks. Based on a synchronous video streaming model and the knowledge of channel state information for next segment, a joint optimization problem of resource allocation and bitrate adaptation is formulated to maximize users’ QoE and minimize energy consumption. The original problem is NP-hard and intractable due to the interference coordination problem introduced by NOMA and the multi-slot sum rate constraint. We decompose the original problem into the subproblem of joint subchannel assignment and power allocation (JSAPA), and the subproblem of bitrate adaptation (BA) with Lagrange relaxation method. A joint resource allocation and bitrate adaptation (JRABA) algorithm is further proposed to solve the JSAPA and BA subproblems iteratively. Specifically, the JSAPA subproblem is solved by an extended Gale-Shapley matching algorithm and differ of convex (DC) programming method iteratively. For the BA subproblem, appropriate video bitrate is explored with subgradient method. Numerical results validate the effectiveness of the proposed algorithm and reveal that network-assisted ABS in NOMA networks benefits users in achieving higher QoE with less energy consumption, especially when radio resources are insufficient or with large segment size.

Artificial Intelligence Aided Joint Bit Rate Selection and Radio Resource Allocation for Adaptive Video Streaming over F-RANs

Recently, fog-computing-based radio access networks (F-RANs) have been conceptualized to provide high quality of experience (QoE) for adaptive bit rate (ABR) streaming, where additional computing capacity is supplemented on fog nodes to facilitate complicated cross-layer optimization (i.e., joint bit rate selection and radio resource allocation). However, finding an optimal global solution with acceptable complexity is still infeasible by the conventional optimization methods. In this work, we propose an artificial intelligence (AI) aided joint bit rate selection and radio resource allocation scheme referred to as iABR, which provides a new vision for handling the over-complicated optimization in F-RANs. Based on multi-agent hierarchy deep reinforcement learning, the proposed iABR can dynamically allocate radio resource and select bit rate in a multiuser scenario, by perceiving the network environment and clients' player information. Moreover, long short-term memory (LSTM) is employed by the iABR algorithm, which enables accurate prediction of the change of channel quality by learning the history of the wireless channel. Hence, iABR is able to adjust the action policy in advance to accommodate the future channel quality for avoiding bit rate fluctuation. According to the experimental results, the iABR exhibits higher QoE in terms of high average bit rate, low rebuffering ratio, and average bit rate variance. Last but not least, the QoE performance of all the clients are fairly guaranteed by the iABR algorithm, enhancing the practicality of AI-driven F-RANs for multimedia service delivery.

Adaptive Bitrate Streaming in Wireless Networks With Transcoding at Network Edge Using Deep Reinforcement Learning

Adaptive bitrate (ABR) streaming has been used in wireless networks to deal with the time-varying wireless channels. Traditionally, wireless video is fetched from remote Internet server. However, wireless video streaming from Internet server faces challenges such as congestion and long latency. ABR streaming and video transcoding at the radio access network (RAN) edge have shown the potential to overcome such problem and provided better video streaming service. In the paper, we consider joint computation and communication for ABR streaming based on mobile edge computing (MEC) under time-varying wireless channels. We propose a joint video transcoding and quality adaptation framework for ABR streaming by enabling RAN with computing capability. By modeling the wireless channel as a finite state Markov channel, we formulate the optimization problem as a stochastic optimization problem of joint computational resource assignment and video quality adaptation for maximizing the average reward, which is defined as the tradeoff between user perceived quality of experience and the cost of performing transcoding at edge server. By using deep reinforcement learning (DRL) algorithm, we develop an automatic algorithm to perform the computational resource assignment and video quality adaptation without any prior knowledge of channel statistics. Simulation results using Tensorflow show the effectiveness of the designed MEC-enabled ABR streaming system and DRL algorithm.

Adaptive Bitrate Seamless Live Streaming over HTTP by Progressive Download Method

Adaptive Bitrate Seamless Live Streaming over HTTP by Progressive Download Method

Analysis of DASH performance over time-varying end-to-end links

Video streaming today shows a widespread success, representing already the greatest portion of the Internet's IP traffic. The most common approach in today's networks is to adopt HTTP-based solutions, such as the Dynamic Adaptive Streaming over HTTP (DASH) protocol. This choice allows using general purpose web clients (web browsers) to access video streaming, while implementing a client-side dynamic rate adaptation of the streaming service. In fact, such HTTP-based approach must cope with intrinsic dynamism of the client's access network, which is a function of concurrent traffic from either the same user or other users sharing the same network resources. In addition, the network virtualization era fosters dynamic management of virtual resources that can lead to even further network variability, with significant changes of the end-to-end link characteristics in terms of bottleneck bandwidth and physical delay. Last but not least, the fruitful utilization of the satellite component in the future 5G networks implies to experience physical delay changes over a larger value range. Therefore, DASH flows will run on communication scenarios with higher and higher variability: this means that the client-side dynamic adaptation feature of DASH becomes a critical component of the whole service. This paper aims to investigate performance of two main DASH Adaptive Bit Rate (ABR) algorithm categories, throughput-based and buffer-based, over a simulated time-varying end-to-end link modeling the joint effects of continuous traffic and link changes. The goal is to provide statistical overview on the DASH ABR adaptation on challenging scenarios in order to draw some baseline recommendations for possible enhancements or modifications. The overall performance analysis is based on a customized script running in the Network Simulator 3 (NS-3).

Hardware-Assisted, Low-Cost Video Transcoding Solution in Wireless Networks

Wireless video streaming has become an extremely popular application in recent years. Internet video streaming to mobile devices, however, faces several challenges, e.g., unstable wireless connections, long latency, high jitter and etc. Bitrate adaptive streaming and video transcoding solutions are widely used to address the above-mentioned issues, however, there are still several shortcomings of these approaches. Such challenges hinder providing satisfactory quality of video streaming service to the mobile users. We propose a hardware-assisted, real-time video transcoding solution implemented on a commercial off-the-shelf device, Raspberry Pi. We employ the software and hardware coupled architecture in order to improve the performance/quality of video streaming and enhance the user satisfactions in wireless network. Our video transcoding solution can be applied to both the downlink and uplink streaming: for downlink stream, it can provide agile bitrate adaptation to sudden network dynamics and enhance video quality by running our transcoding solution at the wireless edge. It can be used to uplink stream for broadcasting live streams in real-time. We present the design and implementation of our video transcoding system in both cases with practical scenarios. The evaluation results reveal that our transcoding solution enhances the performance of video streaming compared with other adaptive bitrate streamings and it provides higher video quality without causing rebuffering or video stall. We bridge the gap between the wireless channel capacity and the video quality while providing a better streaming experience to end user.

DQ-DASH

The significant popularity of HTTP adaptive video streaming (HAS), such as Dynamic Adaptive Streaming over HTTP (DASH), over the Internet has led to a stark increase in user expectations in terms of video quality and delivery robustness. This situation creates new challenges for content providers who must satisfy the Quality-of-Experience (QoE) requirements and demands of their customers over a best-effort network infrastructure. Unlike traditional single server DASH, we developed a D istributed Q ueuing theory bitrate adaptation algorithm for DASH (DQ-DASH) that leverages the availability of multiple servers by downloading segments in parallel. DQ-DASH uses a M x /D/1/K queuing theory based bitrate selection in conjunction with the request scheduler to download subsequent segments of the same quality through parallel requests to reduce quality fluctuations. DQ-DASH facilitates the aggregation of bandwidth from different servers and increases fault-tolerance and robustness through path diversity. The resulting resilience prevents clients from suffering QoE degradations when some of the servers become congested. DQ-DASH also helps to fully utilize the aggregate bandwidth from the servers and download the imminently required segment from the server with the highest throughput. We have also analyzed the effect of buffer capacity and segment duration for multi-source video streaming.

VCC-DASH: A Video Content Complexity-Aware DASH Bitrate Adaptation Strategy

Traditional DASH (dynamic adaptation streaming over HTTP(i.e., HyperText Transfer Protocol)) bitrate strategy cannot differentiate segments with different complexities of video content, resulting in the user’s QoE (quality of experience) of segments with high content complexity as worse than that with low content complexity. In case of this, this paper firstly studies video coding and puts forward the definition of video content complexity. Then the effects of content complexity on user’s QoE is analyzed and the QoE utility function of the segment is formulated based on its MOS (mean opinion score, related to the content complexity and bitrate) and bitrate switching between consecutive segments. Last, in order to maximize user’s QoE, this paper proposes VCC-DASH (video content complexity-aware DASH bitrate adaptation strategy) under the constraints of the network bandwidth and the buffer occupancy. In simulations, we compare VCC-DASH with the classical bitrate adaptation strategy proposed by Liu et al. (LIU’s strategy, for short). The simulation results show that the two strategies have similar performances in bitrate switching numbers, playback interruption times, and buffer lengths. In addition, it is more important for simulation results to reveal that VCC-DASH’s average bitrate is much higher than that of LIU’s strategy, which means that VCC-DASH can make fuller use of the network bandwidth than LIU’s strategy does. Moreover, the MOS distribution of the VCC-DASH is more concentrated on the better scores “4~5”, which profit from its content complexity-aware adaptation to allocate more bandwidth resources to high-complexity segments.

Statistical characterization of tile decoding time of HEVC-encoded 360° video

In this paper, we present a statistical characterization of tile decoding time of 360° videos encoded via HEVC that considers different tiling patterns and quality levels (i.e., bitrates). In particular, we present results for probability density function estimation of tile decoding time based on a series of experiments carried out over a set of 360° videos with different spatial and temporal characteristics. Additionally, we investigate the extent to which tile decoding time is correlated with tile bitrate (at chunk level), so that DASH-based video streaming can make possible use of such an information to infer tile decoding time. The results of this work may help in the design of queueing or control theory-based adaptive bitrate (ABR) algorithms for 360° video streaming.

Exploiting the layer correlation to improve DASH scheduling with scalable video coding

DASH is a promising video streaming solution that gracefully deals with network throughput fluctuations as well as terminal heterogeneity. In DASH, an MPD file describes the available video qualities and their average bitrates. However, the coarse-grained average bitrate is insufficient for an accurate client-side scheduler. The bitrate overestimate or underestimate of bandwidth requirements for segments to be fetched could result in suboptimal scheduling decisions and possibly drain the playback buffer. In this paper, we first made the observation that with SVC, video segments across different layers exhibit high size correlation. We then showed that enhancement layer segment size can be predicted by the corresponding base layer segment with relatively high accuracy. Based on this observation, we enhanced existing rate-based and buffered-based adaptive bitrate (ABR) algorithms with a size predictor, which is orthogonal to existing adaptation logics. Results show that augmented by the size predictor, the average playback bitrate can be improved by up to 20% and playback stalls can also be significantly reduced. Our results demonstrated that similar to the bandwidth predictor, a size predictor should also be added to SVC-based ABR algorithms to increase the performance gain.

Vabis: Video Adaptation Bitrate System for Time-Critical Live Streaming

With the rise of time-critical and interactive scenarios, ultra-low latency has become the most urgent requirement. Adaptive bitrate (ABR) schemes have been widely used in reducing latency for live streaming services. However, the traditional solutions suffer from a key limitation: they only utilize coarse-grained chunk to solve the I-frame misalignment problem in different bitrate switching process at the cost of increasing latency. As a result, existing schemes are difficult to guarantee the timeliness and granularity of control in essence. In this paper, we use a frame-based approach to solve the I-frame misalignment problem and propose a video adaptation bitrate system (Vabis) in units of the frame for time-critical live streaming to obtain the optimal quality of experience (QoE). On the server-side, a Few-Wait ABR algorithm based on Reinforcement Learning (RL) is designed to adaptively select the bitrate of future frames by state information that can be observed, which can subtly solve the problem of I-frame misalignment. A rule-based ABR algorithm is designed to optimize the Vabis system for the weak network. On the client-side, three delay control mechanisms are designed to achieve frame-based fine-grained control. We construct a trace-driven simulator and the real live platform to evaluate the comprehensive live streaming performance. The results show that Vabis is significantly better than the existing methods with decreases in an average delay of 32%-77% and improvements in average QoE of 28-67%.

Joint bit and power loading for adaptive MIMO OFDM VLC systems

AbstractVisible light communication (VLC) is a short‐range wireless access technology based on the dual use of illumination structure. The frequency‐selective characteristics of VLC channels motivate the use of orthogonal frequency division multiplexing (OFDM). In addition, the presence of multiple light sources in most indoor spaces makes multiple‐input–multiple‐output (MIMO) communication techniques a natural solution for VLC systems. In this paper, we investigate the design of an adaptive MIMO OFDM system. Specifically, we propose adaptive bit and power loading for a direct current–biased OFDM VLC system with MIMO mode switching between repetition coding and spatial multiplexing. We formulate the adaptive algorithm design as an optimization problem where we aim to maximize spectral efficiency through the proper selection of modulation order, power level, and MIMO mode, while satisfying a targeted bit error rate. To solve the underlying NP‐hard problems, we use a simulated annealing heuristic. We present Monte Carlo simulation results for a typical indoor setting and illustrate the performance improvements through link adaptation.

A new ADCS method based on guided filter for tea HSIs

To improve the reconstructed performance of mass data storage, transmission and preserving spectral characteristics for tea hyperspectral images(HSIs), a new adaptive distributed compressive sensing method based on guided filter (ADCSGF) is proposed. According to the spectral characteristics of tea HSIs, all bands can be divided into different parts which can be further grouped with different band count. Bands of each group are compressed and reconstructed by distributed compressive sensing method, in which the adaptive bit stream allocation strategy based on the residual error is used to obtain the target bit rate for each non-key band and the key band is regarded as the guided filter band to improve the quality of reconstructed non-key bands in each group. The experimental results showed that ADCSGF can improve the subjective quality of image reconstruction and achieve at least a 1.5 dB higher peak signal-to-noise ratio (PSNR) of spectral dimension decorrelation method (SSDC) than that of distributed compressive sensing based on guided filter (DCSGF). ADCSGF can obtain better reconstructed spectral curve at the sampling rate of 0.1Bpp(Bytes per pixel) and achieve better normalized root mean square error (RMSE) performance at the sampling rate from 0.2Bpp to 0.5Bpp than those of DCSGF.