Mobile Video Streaming Research Articles

Energy and QoE Optimization for Mobile Video Streaming with Adaptive Brightness Scaling

Brightness scaling (BS) is an emerging and promising technique with outstanding energy efficiency on mobile video streaming. However, existing BS-based approaches totally neglect the inherent interaction effect between BS factor, video bitrate and environment context. Their combined impact on user’s visual perception in mobile scenario, leading to inharmonious between energy consumption and user’s quality of experience (QoE). In this paper, we propose PEO , a novel user- P erception-based video E xperience O ptimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between a device’s state of motion, video quality and the resulting user-perceived quality. Specifically, by capturing the motion of the on-the-run device, PEO first infers the optimal bitrate and BS factor, therefore avoiding bitrate-inefficiency for energy saving while guaranteeing the user-perceived QoE. On that basis, we formulate the device motion-aware and user perception-aware video streaming as an optimization problem where we present an optimal algorithm to maximize the object function and adapt to user preference, and thus propose an online bitrate selection algorithm. Our evaluation (based on trace analysis and user study) shows that, compared with state-of-the-art techniques, PEO can raise the perceived quality by 23.8%-41.3% and save up to 25.2% energy consumption.

Learning Audio and Video Bitrate Selection Strategies via Explicit Requirements

Mobile video streaming dominates today's network traffic, and adaptive bitrate (ABR) algorithms have been routinely adopted for transmitting media content across dynamic mobile networks. State-of-the-art ABR algorithms mainly alter video bitrate without considering audio bitrate as they consider the impact on the video negligible due to their small size. However, to bring users an immersive experience, recent content providers have applied high-quality audio with large sizes, like stereophonic sound. Therefore, improper audio bitrate selection will adversely affect video bitrate selection, leading to undesirable audio/video combinations (the highest video quality with the lowest audio quality, and vice versa) and frequent playback interruptions. To address these inefficiencies, we propose a Self-Play reinforcement learning-based Audio-aware ABR algorithm named SPA to learn strategies for audio and video bitrate selections. By learning from explicit goals, SPA can match the actual requirements and attain good performance. By conducting trace-driven and testbed-based experiments, we observe SPA's considerable superiority compared to existing approaches, including reducing the undesirable combinations by up to 34.17× and achieving zero stall time across 88.57% of traces. We also invite 35 volunteers to join a subjective test, and the result shows that 33/35 people consider SPA provides them with a satisfactory viewing experience.

QoE-Based Performance Comparison of AVC, HEVC, and VP9 on Mobile Devices with Additional Influencing Factors

While current video quality assessment research predominantly revolves around resolutions of 4 K and beyond, targeted at ultra high-definition (UHD) displays, effective video quality for mobile video streaming remains primarily within the range of 480 p to 1080 p. In this study, we conducted a comparative analysis of the quality of experience (QoE) for widely implemented video codecs on mobile devices, specifically Advanced Video Coding (AVC), its successor High-Efficiency Video Coding (HEVC), and Google’s VP9. Our choice of 720 p video sequences from a newly developed database, all with identical bitrates, aimed to maintain a manageable subjective assessment duration, capped at 35–40 min. To mimic real-time network conditions, we generated stimuli by streaming original video clips over a controlled emulated setup, subjecting them to eight different packet-loss scenarios. We evaluated the quality and structural similarity of the distorted video clips using objective metrics, including the Video Quality Metric (VQM), Peak Signal-to-Noise Ratio (PSNR), Video Multi-Method Assessment Fusion (VMAF), and Multi-Scale Structural Similarity Index (MS-SSIM). Subsequently, we collected subjective ratings through a custom mobile application developed for Android devices. Our findings revealed that VMAF accurately represented the degradation in video quality compared to other metrics. Moreover, in most cases, HEVC exhibited an advantage over both AVC and VP9 under low packet-loss scenarios. However, it is noteworthy that in our test cases, AVC outperformed HEVC and VP9 in scenarios with high packet loss, based on both subjective and objective assessments. Our observations further indicate that user preferences for the presented content contributed to video quality ratings, emphasizing the importance of additional factors that influence the perceived video quality of end users.

AMIS-MU: Edge Computing Based Adaptive Video Streaming for Multiple Mobile Users

The increasing demand for online high-quality video streaming has brought huge challenges to the traditional client-server video streaming systems due to the high feedback delay, rigorous bandwidth requirement, and the lack of a mechanism of centralized resource management between users. In this work, we propose AMIS-MU, an edge computing-based mobile video streaming system that optimizes the watching experience of users via playback adaptation and channel resource allocation. AMIS-MU fully explores the power of edge servers from three perspectives. First, by pre-caching videos from the cloud, AMIS-MU analyzes video contents at the edge, and achieves a nearly imperceptible content-based playback speed adaptation. Second, as the edge server controls the channel resources of users in a centralized fashion, AMIS-MU adaptively updates the channel configuration to optimize the overall watching experience. Last, the plenty of computational power available at the edge enables a more intelligent playback control by using deep reinforcement learning (DRL). We propose a novel usage of DRL which significantly reduces the complexity of the cross-layer joint optimization problem and solve the non-convex channel resource allocation problem by Lyapunov optimization. Experiments show that AMIS-MU outperforms other existing algorithms in terms of average QoE and fairness.

SenseQ: Context-Aware Video Quality Adaptation for Optimal Mobile Video Streaming in Dynamic Environments

SenseQ: Context-Aware Video Quality Adaptation for Optimal Mobile Video Streaming in Dynamic Environments

Edge Selective Sharing for Massive Mobile Video Streaming With Cross-Layer Optimization

Edge Selective Sharing for Massive Mobile Video Streaming With Cross-Layer Optimization

6G-Powered Efficient Resource Control through IRS-UE Association.

The widespread popularity of live streaming, cloud gaming, mobile video streaming, and many real-time applications relies on high-speed data to ensure low latency and seamless user experience. This large high-speed data demand has led to the development of next-generation or sixth-generation (6G) communication technology. It aims to offer high-speed communication support to multiple applications and interactive services simultaneously. But the vulnerability of node communication to the changing propagation environment often leads to call drops, data loss, and high latency. This paper presents a 6G-enabled wireless network that makes use of multiple intelligent reflecting surfaces (IRSs). The distributed IRSs enhance the robustness of transmission but the increased overhead owing to multiple IRSs is the main challenge. To overcome this, efficient resource control is introduced, which associates sets of IRSs to user equipment (UE). An algorithm, namely IUABP (IRS-UE association based on pilots), is proposed; it offers selective resource control. Furthermore, the performance of the distributed IRS system is evaluated based on the achievable sum rate for different IRS numbers, reflecting elements, and transmit powers. We observed that the proposed association scheme offers an improvement of 30% in the achieved sum rate using N = 50 and R = 5 at a transmit power of 12 dBm. We also discuss the comparison with two other association schemes, namely, distance-based association and random association.

Soft Delivery: Survey on a New Paradigm for Wireless and Mobile Multimedia Streaming

The increasing demand for video streaming services is the key driver of modern wireless and mobile communications. Although many studies have designed digital-based delivery schemes to send video content over wireless and mobile networks, significant quality degradation, known as cliff and leveling effects, often occurs owing to fluctuating channel characteristics. In this article, we present a comprehensive summary of soft delivery, which is a new paradigm for wireless and mobile video streaming and discuss the future directions of soft delivery. Existing studies found that introducing multi-dimensional cosine transform, human vision system, and graph signal processing can make soft delivery schemes more effective in untethered immersive experiences, including virtual reality and volumetric media, than digital-based delivery schemes. In addition, this study finds that soft delivery has the potential to be a new standard to deliver deep neural network models and tactile information over wireless and mobile networks.

Modeling Video Streaming Quality of Experience using Taguchi and Fuzzy Logic Methods

The popularity of mobile video streaming has increased significantly in recent years, and is expected to account for two-thirds of global internet traffic in the near future. However, determining accurately end-users' satisfaction based on network parameters remains a challenge. Existing research often uses network parameters, such as packet loss, delay, and jitter, to estimate users' Quality of Experience (QoE). However, most models present QoE estimates in Mean Opinion Scores (MoS), which are not easily understood by the customers. In this study, we used the Taguchi approach to conduct QoE experiments over a wireless tested. We investigated the simultaneous effects of packet loss, corruption, delay, and jitter on video streaming QoE, as well as their interaction effects. Furthermore, we developed a Fuzzy logic model in MatlabR2016a to establish the relationship between input variables and video streaming QoE. The model presents the results in an easily understandable linguistic terms such as excellent, good, average, bad, and poor. Additionally, the proposed model achieves a correlation of 0.875 between the predicted and user scores, with a Root Mean Square Error of 0.344.

Online Streaming Platform

Video traffic demand over mobile networks have been difficult task, the quality of service get reduced when there is a gap between the traffic demand and link capacity. The quality of video streaming gets poor when there occurs the long buffering and intermittent disruptions. In the cloud computing technology, we propose a new mobile video streaming framework consist of AMES cloud, which is dubbed with: adaptive mobile video streaming and efficient social video sharing these construct a private agent to provide video streaming efficiently for each mobile user. AMO uses the scalable video coding technique to adjust the streaming. SOV monitors the social network interactions among mobile users and as to pre fetch video in advance. The website will have a user-friendly interface, allowing users to easily navigate and access the content of the website. The website will also provide features such as creating playlists, sharing content with friends, and rating videos. Additionally, the website will provide a way for users to upload and publish their own videos. The website will also include a search engine to help users find content quickly and easily.

L-Shape advertising for mobile video streaming services: Less intrusive while still effective

Advertising is significant in video streaming services on mobile devices, as well as in other e-business services. Advertisement providers pay part or the entire service cost; hence, users enjoy services at a low price or free of charge. However, users perceive advertisements as clutter and avoid them. To reduce advertising avoidance and enhance advertising efficiency, various methods, such as raising advertising relevance, seeking advertisement permission, or changing advertisement position, are suggested. This study introduces L-Shape advertising, a new strategy suitable for mobile video streaming services, as it is less distractive and effective. We demonstrate the superiority of L-Shape advertising compared to popular advertising formats such as skippable and non-skippable advertisements. The study empirically tests how the proposed strategy performs and examines users’ cognitive processes applying eye-tracking technology. We found that L-Shape advertising is less distractive but still effective.

Mobility-Based Proactive Cache and Transmission Strategy in Millimeter Wave Heterogeneous Networks

Millimeter wave (mmWave) frequencies with rich bandwidth can greatly alleviate the surging traffic demand of wireless communication from mobile video streaming. However, the harsh propagation environment at mmWave bands and user mobility make it hard to provide a reliable service. Multibeam transmissions can be taken as an essential approach for maintaining a robust mmWave link. Meanwhile, the introduction of coded caching can further reduce the end-to-end latency and backhaul traffic. Considering the interaction between caching and transmission in mobility scenario, we develop a joint design for mmWave heterogeneous networks (HetNets), by combining multibeam transmission and maximum distance separable (MDS)-coded caching. First, a joint optimization problem of cache placement and beam pair selection is formulated, in order to maximize the backhaul traffic offload in mmWave HetNets. To solve the mixed-timescale mixed-integer nonlinear programming (MINLP) problem, we then decompose the joint problem into the long-term mobility-based proactive content caching (CC) and short-term content delivery (CD) subproblems. Subsequently, two many-to-many matching-based algorithms are proposed for tackling the CD subproblem and the convex technique is applied to deal with the CC subproblem. Simulation results demonstrate the efficacy of the proposed algorithms over benchmark schemes.

Intelligent Caching for Mobile Video Streaming in Vehicular Networks with Deep Reinforcement Learning

Caching-enabled multi-access edge computing (MEC) has attracted wide attention to support future intelligent vehicular networks, especially for delivering high-definition videos in the internet of vehicles with limited backhaul capacity. However, factors such as the constrained storage capacity of MEC servers and the mobility of vehicles pose challenges to caching reliability, particularly for supporting multiple bitrate video streaming caching while achieving considerable quality of experience (QoE). Motivated by the above challenges, in this paper, we propose an intelligent caching strategy that takes into account vehicle mobility, time-varying content popularity, and backhaul capability to improve the QoE of vehicle users effectively. First, based on the mobile video mean opinion score (MV-MOS), we designed an average download percentage (ADP) weighted QoE evaluation model. Then, the video content caching problem is formulated as a Markov decision process (MDP) to maximize the ADP weighted MV-MOS. Owing to the prior knowledge of video content popularity and channel state information that may not be available at the road side unit in practical scenarios, we propose a deep reinforcement learning (DRL)-based caching strategy to solve the problem while achieving a maximum ADP weighted MV-MOS. To accelerate its convergence speed, we further integrate the prioritized experience replay, dueling, and double deep Q-network technologies, which improve the performance of DRL algorithm. Numerical results demonstrate that the proposed DRL-based caching strategy significantly improves QoE, and achieves better video delivery reliability compared to existing non-learning approaches.

Sprinkle Prebuffer Strategy to Improve Quality of Experience with Less Data Wastage in Short-Form Video Streaming

In mobile short-form video streaming, the video application usually provides the user with a playlist of recommended videos to be played one by one. In order to prevent playback stalls caused by possible fluctuations in the mobile network, after finishing buffering the currently-playing video, commercial video players continue to prebuffer (i.e., buffer in advance before playback) one subsequent video in the playlist with as much content as possible. However, since the user can skip a video at any time if he/she does not like it, prebuffering too much video content leads to the wastage of mobile data. Contrarily, without prebuffering any subsequent video, the video player is exposed to high risks of stalling events, which threaten the user’s quality of experience (QoE). In this paper, a novel Sprinkle Prebuffer Strategy (SPS) is proposed to overcome such drawbacks. Once the currently-playing video’s buffer reaches an optimal buffer threshold, the proposed SPS attempts to concurrently prebuffer all subsequent videos in the playlist, each up to an optimal prebuffer threshold. Based on the evaluation results, it is proven that the proposed SPS outperforms the referenced methods in providing the best user’s QoE with reasonable compensation for data wastage.

NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video Streaming

Video streaming services strive to support high-quality videos at higher resolutions and frame rates to improve the quality of experience (QoE). However, high-quality videos consume considerable amounts of energy on mobile devices. This paper proposes NeuSaver, which reduces the power consumption of mobile devices when streaming videos by applying an adaptive frame rate to each video chunk without compromising the user experience. NeuSaver generates a policy that can determine the appropriate frame rate for each video chunk using reinforcement learning (RL). The RL model automatically learns the policy that optimizes the QoE goals based on previous observations. NeuSaver also uses an asynchronous advantage actor-critic algorithm to reinforce the RL model quickly and robustly. Streaming servers that support NeuSaver preprocess videos into segments with various frame rates, which is similar to the process of creating videos with multiple bit rates in dynamic adaptive streaming over HTTP. NeuSaver utilizes the commonly used H.264 video codec. We evaluated NeuSaver in various experiments and a user study through four video categories along with the previously proposed model. Our experiments showed that NeuSaver effectively reduces the power consumption of mobile devices when streaming video by an average of 16.14% and up to 23.12% while maintaining high QoE.

Content-Adaptable ROI-Aware Video Storage for Power-Quality Scalable Mobile Streaming

The demand for mobile video streams is constantly increasing. With this demand comes a need for mobile devices to receive more videos at ever increasing quality. However, due to the large size of video data and intensive computational requirements, video streaming requires frequent memory access that consume a substantial amount of mobile device power; as a result, the battery life of mobile devices is limited. In this paper, we present a video content-adaptable Region-of-Interest (ROI)-aware video storage technique that promotes power savings. During the video encoding process on the transmitting server, based on the macroblock variance and ROI characterization, the “macroblocks of interest” are identified and embedded in the encoded bitstream. In the decoding process, a new frame buffer with dynamic power-quality trade-off is presented to adapt to the macroblock characteristics during run-time. Results from the system-level and circuit-level simulations show that the proposed technique enables substantially more truncated bits and significant power savings while delivering similar or better video quality as compared to other state-of-the-art solutions.

An Edge Caching Strategy Based on User Speed and Content Popularity for Mobile Video Streaming

Mobile users’ demands to delay-sensitive video streaming media put forward new requirements for mobile networks, such as architecture optimization. Edge caching as a new paradigm is proposed to enhance the quality of service (QoS) for mobile users at the network edge. Due to the limited coverage of edge cache nodes, the frequent handoffs between base stations would aggravate network traffic overhead, resulting in a bad experience of high latency and service interruption when mobile users browse videos. This paper first proposes a three-layer mobile edge network architecture and applied edge caching to video streams to build an efficient caching system. Given the user’s mobility and low latency of mobile video streaming, we propose an edge caching strategy based on user speed and content popularity. Horizontally, the user’s speed affects the spanning area and the buffer size of the cache on edge; vertically, content popularity determines the priority of cached videos. Experimental results demonstrate that our caching strategy outperforms other schemes in terms of the average delay and the cache hit ratio in mobile video streaming scenes compared with the other three classic caching methods.

Post-Streaming Wastage Analysis – A Data Wastage Aware Framework in Mobile Video Streaming

Mobile video streaming is now ubiquitous among mobile users. This work investigates a less studied and yet significant problem in mobile video streaming – data wastage, i.e., some downloaded video data may not be played back but discarded by video players due to early departure or video skip, thus the bandwidth consumed in transferring them is wasted. Our measurements show that data wastage is significant in practice, e.g., 25.2 percent∼51.7 percent of video data downloaded are in fact wasted. Moreover, substantial data wastage exists not only in current commercial streaming platforms, but also in state-of-the-art adaptive streaming systems proposed in the literature. This work develops a new post-streaming wastage analysis (PSWA) framework to tackle this problem by converting existing adaptive streaming algorithms into data wastage aware versions. PSWA enables the streaming vendors to explicitly control the tradeoff between data wastage and quality-of-experience (QoE). Extensive evaluations show that PSWA can reduce data wastage significantly (e.g., 80 percent) without any adverse impact on QoE. Moreover, it has strong robustness to perform consistently across a wide range of networks. PSWA can be readily implemented into current streaming platforms, and thus offers a practical solution to data wastage for mobile streaming services.

MEC-assisted flexible transcoding strategy for adaptive bitrate video streaming in small cell networks

Adaptive bitrate video streaming (ABR) has become a critical technique for mobile video streaming to cope with time-varying network conditions and different user preferences. However, there are still many problems in achieving high-quality ABR video streaming over cellular networks. Mobile Edge Computing (MEC) is a promising paradigm to overcome the above problems by providing video transcoding capability and caching the ABR video streaming within the radio access network (RAN). In this paper, we propose a flexible transcoding strategy to provide viewers with low-latency video streaming services in the MEC networks under the limited storage, computing, and spectrum resources. According to the information collected from users, the MEC server acts as a controlling component to adjust the transcoding strategy flexibly based on optimizing the video caching placement strategy. Specifically, we cache the proper bitrate version of the video segments at the edge servers and select the appropriate bitrate version of the video segments to perform transcoding under jointly considering access control, resource allocation, and user preferences. We formulate this problem as a nonconvex optimization and mixed combinatorial problem. Moreover, the simulation results indicate that our proposed algorithm can ensure a low-latency viewing experience for users.

Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming

Video streaming application such as Youtube is one of the most popular mobile applications. To adjust the quality of video for available network bandwidth, a streaming server provides multiple representations of video of which bit rate has different bandwidth requirements. A streaming client utilizes an adaptive bit rate (ABR) scheme to select a proper representation that the network can support. However, in mobile environments, incorrect decisions of an ABR scheme often cause playback stalls that significantly degrade the quality of user experience, which can easily happen due to network dynamics. In this work, we propose a control theory (Linear Quadratic Optimization)-based ABR scheme to enhance the quality of experience in mobile video streaming. Our simulation study shows that our proposed ABR scheme successfully mitigates and shortens playback stalls while preserving the similar quality of streaming video compared to the state-of-the-art ABR schemes.