Streaming In Wireless Networks Research Articles

Online tile dispatching framework with guarantees for 360-degree video streaming in wireless networks

Online tile dispatching framework with guarantees for 360-degree video streaming in wireless networks

AP-assisted adaptive video streaming in wireless networks with high-density clients

Adaptive video streaming over wireless networks has experienced tremendous growth in past few years. In order to guarantee users’ quality of experience (QoE), adaptive bitrate (ABR) algorithms have been extensively studied. With the recent emergency of high-density Wi-Fi networks, these solutions no longer perform well. On the one hand, bitrate decisions respond slowly to high network fluctuations, and on the other hand, wireless channel resources are insufficient especially under the cases that multiple clients compete for the limited bandwidth. Hence, users’ personalized QoE metrics need to be taken into account to cope with the QoE degradation. To this end, we design an access point (AP) assisted wireless dynamic adaptive video streaming over Hypertext Transfer Protocol (HTTP) solution called Wi-DASH, which aims to improve users’ QoE while considering channel utilization. In Wi-DASH, the video server aggregates real-time network status with the assistance of AP, and estimates clients’ player status through statistical analysis of chunk request logs. On this basis, a deep reinforcement learning (DRL) based ABR algorithm is designed, where the DRL model can deal with the complicated global status information including network status, player status, and QoE preferences. Finally, we implement Wi-DASH system, and conduct experiments with 4K resolution videos. Experimental results reveal that the Wi-DASH can more fully utilize wireless channel resources, and significantly improve users’ QoE.

Power efficient UAV placement and resource allocation for adaptive video streaming in wireless networks

Unmanned aerial vehicle (UAV) enabled wireless communication that can transmit/receive data to/from ground users, especially at cell boundaries or in disaster-stricken areas where the ground-based infrastructure has been damaged, is an emerging area of research. These aerial vehicles require energy for communication and are typically energy constrained. The communication system becomes more energy hungry when it comes to video streaming of high quality. Because for higher video quality streaming, higher bitrate and more bandwidth is required. Higher transmission power and rate will result in increased streaming quality. We concentrate on UAV-aided power efficient adaptive video streaming wireless networks with multiple ground users, taking into account the available energy budget and information causality constraints. The objective of this research work is to maximize adaptive video streaming utility per unit of power in a UAV-enabled wireless communication network by optimizing the position of UAV and allocation of communication resources, such as bandwidth, and transmit power, while competing with system-designed constraints, such as energy budget, transmission rate, and allocated bandwidth. The problem that is addressed is non-convex which is approached by applying successive convex approximation, Dinkelbach method and coordinates decent techniques, and an efficient iterative algorithm is devised for its solution. Extensive simulations show that the suggested optimization strategy outperforms the results of the previously proposed works.

Enhancing Media Streaming in Wireless Networks using IFW-CFH Algorithm

One of the major concerns for service providers and application developer are Quality of experience (QoE), where high traffic congestion on the Internet leads to the degradation of video quality. However, the effectiveness of video transmission is minimized due to the network based on packet loss, bandwidth, and delay. Because of bandwidth limitations, the videos transmitted are obtained in low quality. Meanwhile, various outcomes such as reduction in throughput, re-buffering, or mosaic are determined in packet loss which validated the video streaming obtained in reliable or unreliable mode. Therefore this paper proposes an Improved Fuzzy Weighted queueing based Crossover Fire Hawk (IFW-CFH) algorithm for effective real-time video transmission. The objective of the IFW-CFH approach is to reduce the delay, packet loss, and bandwidth to enhance the video quality via two key mechanisms namely congestion control mechanism as well as packet scheduling mechanism. During the generation of encoded video frames, the packaged packets to the local buffer are transmitted by the scheduler using our proposed IFW-CFH algorithm. Finally, the experimentation is conducted and the results show that the proposed method minimized transmission delay, packet loss, and bandwidth by 13.8% for effective real-time video transmission compared to the existing methods.

An adaptive cache switching based video streaming using Buffer seeded multi checkpoint peer responder to improve the quality of service in WSN

The widespread of online video access by the various user become huge in recent days by direct streaming through the internet. However, video streaming in wireless networks consumes more delay due to response from the service provider. Due to high bandwidth consideration, the quality of streaming video consumes more buffer state to load the data. By this mitigation, the loss quality becomes problematic to access the service continually. To resolve this problem, we propose an On-Demand Service-Based Peer Switching (ODSBPS) for video streaming using Buffer seeded multi checkpoint peer responded to improve the quality of service in WSN. Initially, these methods verify the service request load based on the energy consumption in multi-channel service responder. Based on the Energy-Aware Traffic Flow Estimation (EATFE) verifies the buffer state from the nearest peer to create a checkpoint. This nearest checkpoint is estimated based on Streaming Service Intense Access Rate (SSIAR). Through this SSIAR checkpoint, the data is loaded on reserved cache points to create buffer points. Simultaneously the nearest peer responder checks the previous checkpoint to load the seed into the peer end. The peer end verifies the last buffer checkpoint to stream continuous service to make low energy level streaming provide the best video streaming service.

Towards Influence of Chunk Size Variation on Video Streaming in Wireless Networks

In recent years, the growth in popularity of mobile video streaming services is unbroken. There are tremendous demands for video streaming over wireless networks. Currently, most video streaming is over HTTP. Up to now, HTTP-based adaptive video streaming is standardized as DASH, where a client-side video player can dynamically pick the bitrate level according to the perceived network conditions. Actually, not only the available bandwidth drastically varies due to wireless network properties, but also the chunk sizes in the same bitrate level significantly fluctuate, which also influences the bitrate adaptation. However, existing bitrate adaptation algorithms mostly focus on available bandwidth but do not involve chunk size variation, leading to performance losses. In this paper, we theoretically analyze the influence of chunk size variation on bitrate adaptation performance in wireless networks. Based on DASH system features, we build a general model describing playback buffer evolution. Applying stochastic theories, we respectively analyze the influence of the chunk size variation on rebuffering probability, average bitrate, and bitrate switching interval. Furthermore, based on theoretical insights, we provide several suggestions for algorithm designing and rate encoding, and also design a simple bitrate adaptation algorithm. Extensive simulations verify our insights, suggestions, and designed algorithm effectiveness.

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.

Stochastic QoE-aware optimization of multisource multimedia content delivery for mobile cloud

The increasing popularity of mobile video streaming in wireless networks has stimulated growing demands for efficient video streaming services. However, due to the time-varying throughput and user mobility, it is still difficult to provide high quality video services for mobile users. Our proposed optimization method considers key factors such as video quality, bitrate level, and quality variations to enhance quality of experience over wireless networks. The mobile network and device parameters are estimated in order to deliver the best quality video for the mobile user. We develop a rate adaptation algorithm using Lyapunov optimization for multi-source multimedia content delivery to minimize the video rate switches and provide higher video quality. The multi-source manager algorithm is developed to select the best stream based on the path quality for each path. The node joining and cluster head election mechanism update the node information. As the proposed approach selects the optimal path, it also achieves fairness and stability among clients. The quality of experience feature metrics like bitrate level, rebuffering events, and bitrate switch frequency are employed to assess video quality. We also employ objective video quality assessment methods like VQM, MS-SSIM, and SSIMplus for video quality measurement closer to human visual assessment. Numerical results show the effectiveness of the proposed method as compared to the existing state-of-the-art methods in providing quality of experience and bandwidth utilization.

Buffer-Aware Streaming in Small-Scale Wireless Networks: A Deep Reinforcement Learning Approach

Buffer-aware video streaming, which exploits the available storage space in user device to store the prefetched video data in good channels for video data use in poor channels, has been proved to have the potential to reduce the impact of fluctuating wireless channels on user-perceived video performance. However, in practical wireless networks, due to the unknown channel state and video rate, providing buffer-aware video streaming service to wireless user is a challenging problem. In this paper, with the aim to design an autonomous wireless video streaming system, we apply the deep reinforcement learning approach to dynamic resource optimization for wireless buffer-aware video streaming under unknown channel state and video rate. Specifically, we define a reward function for buffer-aware video streaming as the effective video streaming time when neither video-playback overflow nor video-playback underflow occurs. We propose a Markov decision process based problem formulation of the joint bandwidth allocation and buffer management for maximizing the effective video streaming time of all users. The optimal bandwidth allocation and buffer management policy is learned from training a deep neural network based on a deep reinforcement learning algorithm. We simulate the proposed algorithm in Tensorflow. Simulation results verify that the proposed deep reinforcement learning approach is effective for buffer-aware video streaming in wireless networks.

무선 환경에서 HTTP 적응적 스트리밍의 QoE 향상을 위한 안정성 기반의 품질 적응 기법

무선 환경에서 HTTP 적응적 스트리밍의 QoE 향상을 위한 안정성 기반의 품질 적응 기법

XMAS: An Efficient Mobile Adaptive Streaming Scheme Based on Traffic Shaping

Adaptive video streaming can provide adequate Quality of Experience by dynamically adjusting video rates in responding to fluctuating service conditions. However, adaptive video streaming shows dismal performances in wireless networks where several players share a wireless bottleneck link. This paper proposes a novel video rate selection scheme called XMAS for efficient video streaming in wireless networks. XMAS consists of two components: an available bandwidth estimation part and a video rate selection part. To redress the problem of inaccurate bandwidth estimation due to peculiar on-off transmission patterns of mobile video streaming, we devised a novel client-based traffic shaping scheme that effectively throttles server's packet transmission. Equipped with accurate bandwidth estimates, XMAS determines target transmission rates considering playback buffer levels. We implemented XMAS on Linux and performed rigorous experiments to analyze its behavior and performance. Our performance results showed that XMAS achieves up to 20% increase in average video rates while reducing rebuffer rates significantly.

무선 환경에서 끊김 없는 HTTP 적응적 스트리밍을 위한 지터 기반 전송률 조절 기법

HTTP 적응적 스트리밍(HTTP adaptive streaming)은 서버가 다양한 품질의 비디오를 저장해놓고 클라이언트가 세그먼트 처리량을 기반으로 가용대역폭을 예측하여 알맞은 품질을 요청함으로 체감 품질을 향상시키는 기법이다. 그러나 대역폭 변화가 빈번하고 높은 손실률을 갖는 무선 환경에서는 실제 네트워크의 대역폭을 측정하는데 어려움이 있다. 대역폭 측정 오류로 인한 빈번한 품질 변화와 재생 끊김 현상은 체감 품질을 저하시킨다. 본 논문에서는 지연의 편차인 지터를 패킷 단위로 측정하고 지터에 따라 가중치를 부여하여 가용대역폭을 측정하는 기법을 제안한다. 제안하는 기법은 급격한 대역폭 변화로 인해 높은 지터가 발생하는 경우 대역폭 변화를 적게 반영하여 비디오 품질 변화를 줄이고 버퍼 언더플로우를 완화시킨다. 실험을 통해 제안하는 기법이 무선 환경에서 버퍼 언더플로우를 완화시키고 빈번한 품질 변화를 감소시켜 체감 품질을 향상시키는 것을 확인하였다.

A QoE-Driven Encoder Adaptation Scheme for Multi-User Video Streaming in Wireless Networks

Video streaming over networks has grown rapidly in recent years. Increasing focus has gradually turned from quality of service awareness to end viewer's quality of experience (QoE) awareness. For multi-user video streaming services, videos are encoded and transmitted through a bandwidth-limited wireless networks. Therefore, proper encoder settings for videos are required to satisfy the total channel rate constraint. In this paper, a QoE-driven High Efficiency Video Coding (HEVC) encoder adaptation scheme is proposed, aiming to maximize QoE of all the users for wireless networks. First, the influence of HEVC encoder on video streaming is investigated to generate an encoder parameter model. Then, the effect of network impairment together with HEVC encoder is taken into account to derive a subjective QoE prediction model. Afterward, we formulate a QoE-maximized encoder adaptation with total channel rate constraint into an optimization problem based on the obtained encoder parameter model and the subjective QoE model. The problem is solved and incorporated into an online encoder adaptation scheme. Compared with conventional rate allocation methods, the proposed QoE-driven encoder adaptation scheme achieves significant QoE gains independent of wireless network channel rate and video content. Simulation results demonstrate that the proposed encoder adaptation scheme can improve the QoE by 31.4% with fixed number of users or increase the number of satisfied users by 35.5% with variable number of users.

Eliminating bandwidth estimation from adaptive video streaming in wireless networks

Dynamic Adaptive Streaming over HTTP (DASH) is the state-of-the-art technology for video streaming and has been widely deployed in both wired and wireless environments. However, mobile DASH users often suffer from video quality oscillation and even video freeze in wireless environments, which results in poor user experience. This is mainly because most quality adaptation algorithms in DASH rely highly on bandwidth estimation to adjust the video quality while wireless network bandwidth is unstable in nature and changes frequently according to wireless channel contention and condition. To provide stable performance, even during severe bandwidth fluctuation, this paper proposes the Wireless Quality Adaptation (WQUAD) algorithm, which eliminates bandwidth estimation from quality adaptation. Thanks to the Scalable Video Codec (SVC), the proposed scheme always prioritizes to lower layers over higher ones as long as the play-out buffer is not completely filled by the lower layers. As a result, the client always fills the buffer with the base layers first and then the upper enhancement layers sequentially. This horizontal adaptation is straightforward and does not require any bandwidth estimation. Through NS-2 simulations, we show that WQUAD achieves (i) stable performance, keeping the video quality level with respect to the long-term network bandwidth, (ii) effective video freeze prevention, and (iii) high video quality on average.

Optimal Rate Allocation for Video Streaming in Wireless Networks With User Dynamics

We consider the problem of optimal rate allocation and admission control for adaptive video streaming sessions in wireless networks with user dynamics. The central aim is to achieve an optimal tradeoff between several key objectives: maximizing the average rate utility per user, minimizing the temporal rate variability, and maximizing the number of users supported. We derive sample path upper bounds for the long-term net utility rate in terms of either a linear program or a concave optimization problem, depending on whether the admissible rate set is discrete or continuous. We then show that the upper bounds are asymptotically achievable in large-scale systems by policies which either deny access to a user or assign it a fixed rate for its entire session, without relying on any advance knowledge of the duration. Moreover, the asymptotically optimal policies exhibit a specific structure, which allow them to be characterized through just a single variable, and have the further property that the induced offered load is unity. We exploit the latter insights to devise parsimonious online algorithms for learning and tracking the optimal rate assignments and establish the convergence of these algorithms. Extensive simulation experiments demonstrate that the proposed algorithms perform well, even in relatively small-scale systems.

Joint Power Control and Rate Adaptation for Video Streaming in Wireless Networks With Time-Varying Interference

This paper considers a cross-layer optimization framework for video streaming in multinode wireless networks with a time-varying interference environment. We develop a distributed joint power control and rate adaptation framework that exploits the time diversity of the wireless channels, satisfies the hard delay constraints associated with video applications, and respects a certain fairness criterion among the nodes. The proposed framework performs power allocation at the physical/media access control layers to achieve a certain target signal-to-interference-plus-noise ratio, such that the difference between the arrival and the departure rates at the queues is very small, and performs video rate adaptation at the video coding layer (upper layer) according to the nodes' demanded video quality, their channel conditions, and a given fairness criterion. A main challenge here is that the adaptation of the video rate and the power control is not performed at the same time scale. We deal with this issue and model the power and rate variations of the nodes as linear stochastic dynamic equations and formulate a risk-sensitive control problem that captures the hard delay constraints of the video services and the fairness criterion for resource utilization. We provide an optimal solution for this control problem and illustrate the performance of our framework through simulations.

Dynamic rate adaptation for adaptive video streaming in wireless networks

In this paper, we investigate the streaming strategy for dynamic adaptive streaming over HTTP (DASH). Specifically, we focus on the rate adaptation algorithm for streaming scalable video (H.264/SVC) in wireless networks. We model the rate adaptation problem as a Markov Decision Process (MDP), aiming to find an optimal streaming strategy in terms of user-perceived quality of services (QoS) such as playback interruption, average playback quality and playback smoothness. We then obtain the optimal MDP solution using dynamic programming. However, the optimal solution requires the knowledge of the available bandwidth statistics and has a large number of states, which makes it difficult to obtain the optimal solution in real time. Therefore, we further propose an online algorithm which integrates the learning and planning process. The proposed online algorithm collects bandwidth statistics and makes streaming decisions in real time. A reward parameter has been defined in our proposed streaming strategy, which can be adjusted to make a good trade-off between the average playback quality and playback smoothness. We also use a simple testbed to validate our proposed algorithm. Experimental results show the feasibility of the proposed algorithm and its advantage over the existing work.

On Managing Quality of Experience of Multiple Video Streams in Wireless Networks

Managing the Quality-of-Experience (QoE) of video streaming for wireless clients is becoming increasingly important due to the rapid growth of video traffic on wireless networks. The inherent variability of the wireless channel as well as the Variable Bit Rate (VBR) of the compressed video streams make QoE management a challenging problem. In this paper, we investigate scheduling algorithms to transmit multiple video streams from a base station to mobile clients. We present an epoch-by-epoch framework to fairly allocate wireless transmission slots to streaming videos. In each epoch, our scheme reduces the vulnerability to stalling by allocating slots to videos in a way that maximizes the minimum “playout lead” across all videos. We show that the problem of allocating slots fairly is NP-complete even for a constant number of videos. We then present a fast lead-aware greedy scheduling algorithm. Our greedy algorithm is optimal when the channel quality of a user remains unchanged within an epoch. Our experimental results, based on public MPEG-4 video traces and wireless channel traces that we collected from a WiMAX test-bed, show that the lead-aware greedy approach results in a fair distribution of stalls across the clients when compared to other algorithms, while still maintaining similar or fewer average number of stalls per client.

Flow-Level QoE of Video Streaming in Wireless Networks

The Quality of Experience (QoE) of streaming service is often degraded by frequent playback interruptions. To mitigate the interruptions, the media player prefetches streaming contents before starting playback, at a cost of delay. We study the QoE of streaming from the perspective of flow dynamics. First, a framework is developed for QoE when streaming users join the network randomly and leave after downloading completion. We compute the distribution of prefetching delay using partial differential equations (PDEs), and the probability generating function of playout buffer starvations using ordinary differential equations (ODEs) for CBR streaming. Second, we extend our framework to characterize the throughput variation caused by opportunistic scheduling at the base station, and the playback variation of VBR streaming. Our study reveals that the flow dynamics is the fundamental reason of playback starvation. The QoE of streaming service is dominated by the first moments such as the average throughput of opportunistic scheduling and the mean playback rate. While the variances of throughput and playback rate have very limited impact on starvation behavior.

Adaptive Multiple TCP-connection Scheme to Improve Video Quality over Wireless Networks

Due to the prevalence of powerful mobile terminals and the rapid advancements in wireless communication technologies, the wireless video streaming service has become increasingly more popular. Recent studies show that video streaming services via Transmission Control Protocol (TCP) are becoming more practical. TCP has more advantages than User Diagram Protocol (UDP), including firewall traversal, bandwidth fairness, and reliability. However, each video service shares an equal portion of the limited bandwidth because of the fair sharing characteristics inherent in TCP and this bandwidth fair sharing cannot always guarantee the video quality for each user. To solve this challenging problem, an Adaptive Multiple TCP (AM-TCP) scheme is proposed in this paper to guarantee the video quality for mobile devices in wireless networks. AM-TCP adaptively controls the number of TCP connections according to the video Rate Distortion (RD) characteristics of each stream and network status. The proposed scheme can minimize the total distortion of all participating video streams and maximize the service quality by guaranteeing the quality of each video streaming session. The simulation results show that the proposed scheme can significantly improve the quality of video streaming in wireless networks.