Variable bitrate (VBR) encoding has gained considerable interest due to its capacity to enhance video quality and mitigate transmission congestion in contrast to constant bitrate (CBR) encoding. However, adaptive bitrate (ABR) streaming faces challenges when dealing with VBR-encoded videos, primarily stemming from the significant variability in chunk size and the consequent bitrate fluctuations. This paper proposes CoarseUCB, a context-aware online learning algorithm for bitrate adaptation in VBR-encoded videos. CoarseUCB considers important aspects of VBR-encoded video streaming and uses the upper confidence bound (UCB) method for bitrate selection. The UCB method does not require precise bandwidth estimation and balances the exploration and exploitation of each action effectively. Additionally, CoarseUCB accounts for the impact of multiple future video chunks when making the bitrate decision for the current chunk. To evaluate the effectiveness of CoarseUCB, we conduct experiments to assess its efficiency. The results show that CoarseUCB delivers a higher average user quality of experience (QoE) compared to state-of-the-art ABR algorithms, resulting in an improvement of up to 9.81%.

Variable Bitrate (VBR) video encoding can significantly improve the quality-of-experience (QoE) of viewing users due to its capability to provide much higher quality-to-bits ratio compared to Constant Bitrate (CBR) video encoding. However, the streaming of VBR-encoded videos suffers from large variance of video chunk size, which may directly result in frequent rebuffering if not properly handled. In this paper, we propose a novel neural-enhanced adaptive streaming framework for VBR-encoded videos called PreSR, which performs selective prefetching of video chunks to achieve a higher QoE for viewers. The design of PreSR is motivated by an important observation obtained from our measurement, namely, the video quality improvement and bandwidth savings brought by neural enhancement are more pronounced for low-resolution video chunks with complex scenes. By taking the above fact and the time required for neural enhancement into account, we formulate the problem into an optimization problem. Given that the problem is NP-hard, we design the PreSR framework, which is based on the model predictive control theory and also considers key features of VBR-encoded videos. PreSR parallelizes the download of video chunks and model inference processes to fully utilize the available compute resources. Finally, we conduct extensive experiments with real traces, and the results show that PreSR outperforms the state-of-the-art algorithms with an improvement up to 11.25% in terms of the average QoE.

The high-definition (HD) video streaming has gained tremendous popularity with the proliferation of smartphones and mobile networks. However, it is quite challenging to deliver HD online video streams directly to devices with very low bandwidth in current systems. In this paper, we propose a neural-enhanced HD video streaming system named PatchSR to provide HD video streaming for bandwidth-constrained devices. PatchSR delivers universal super-resolution (SR) models with high performance to devices in advance. Only low-resolution video streams are sent to bandwidth-constrained devices, and the video quality at the device side can be enhanced with SR techniques. The main challenge is training multiple universal SR models with high performance and selecting the dedicated SR model for each video content. To overcome this new challenge, we propose an image classification algorithm of texture features according to the Discrete Fourier Transform (DFT) feature map of the training patch. We also design a dynamic selection algorithm of SR models for clients to improve video quality. Finally, we achieve and evaluate our proposed PatchSR system with real network traces and the experimental results show that PatchSR achieves higher video quality and up to 28.65% QoE improvement compared to baselines.

In heterogeneous cellular network, task scheduling for computation offloading is one of the biggest challenges. Most works focus on alleviating heavy burden of macro base stations by moving the computation tasks on macro cell user equipment (MUE) to remote cloud or small cell base stations. But the selfishness of network users is seldom considered. Motivated by the multiple access mobile edge computing, this paper provides incentive for task transfer from macro cell users to small cell base stations. The proposed incentive scheme utilizes small cell user equipments to provide relay services. The problem of computation offloading is modeled as a two-stage auction, in which the remote MUEs with common social character can form a group and then buy the computation resource of small cell base stations with the relaying of small cell user equipment. A two-stage auction scheme named TARCO is contributed to maximize utilities for both sellers and buyers in the network. The truthfulness, individual rational and budget balance properties of TARCO are also proved in this paper. In addition, two algorithms are proposed to further refine TARCO on the social welfare of the network. One can achieve higher utility of MUEs and the other can obtain higher total social welfare. Extensive simulation results demonstrate that, TARCO is better than random algorithm by 104.90 percent in terms of average utility of MUEs, while the performance of TARCO is further improved up to 28.75 percent and 17.06 percent by the proposed two algorithms, respectively.

In online HPC education platforms, a large amount of educational resources are in the form of video. It is desirable to provide better QoE (Quality of Experience) for students when they are viewing these educational resources. Compared to traditional constant bitrate (CBR) encoding, variable bitrate (VBR) encoding can achieve better video quality and reduce network bandwidth. However, previous adaptive bitrate (ABR) schemes were commonly designed for CBR encoded videos. Such ABR schemes are not suitable to stream VBR encoded videos whose chunk sizes fluctuate rapidly. In this paper, we propose a novel ABR scheme call VBSSR, which takes the characteristics of VBR encoded video into consideration. The basic idea of VBSSR is to stream video chunks with complex scenes at a low bitrate level to reduce bandwidth consumption, and then boost the video quality by leveraging the technique of super-resolution (SR) at the client-side. VBSSR trains a deep reinforcement learning (DRL) based neural model to jointly make bitrate selections and decide which chunks to be enhanced. We conduct extensive trace-driven evaluations to compare VBSSR with other state-of-the-art methods. The experiment results show that our method significantly outperforms existing approaches with improvements in the average video quality by at least 37.1% while reducing the rebuffering time by 24.3%–75.9%.

Variable Bitrate (VBR) video encoding can provide much high quality-to-bits ratio compared to the widely adopted Constant Bitrate (CBR) encoding, and thus receives significant attentions by content providers in recent years. However, it is challenging to design efficient adaptive bitrate algorithms for VBR-encoded videos due to the sharply fluctuating chunk size and the resulting bitrate burstiness. In this paper, we propose a neural adaptive streaming framework called Vibra for VBR-encoded videos, which can well accommodate the high fluctuation of video chunk sizes and improve the quality-of-experience (QoE) of end users significantly. Our framework takes the characteristics of VBR-encoded videos into account, and adopts the technique of deep reinforcement learning to train a model for bitrate adaptation. We also conduct extensive trace-driven experiments, and the results show that Vibra outperforms the state-of-the-art ABR algorithms with an improvement of 8.17% -- 29.21% in terms of the average QoE.

Vehicular fog computing (VFC) has become an appealing paradigm to provide services for vehicles and traffic systems. However, high mobility is one of the great challenges to the communication and computation service qualities in VFC. A network model for directional vehicle mobility is proposed in this paper to guarantee the service qualities of vehicles in VFC. In the model, vehicles are configured into three vehicular subnetworks according to their turning directions at the next crossing. For each subnetwork, vehicles communicate with each other via vehicle-to-vehicle communication, and with roadside units via vehicle-to-infrastructure communication. The aim is to minimize the average response time of the tasks originated from vehicles. By carefully choosing neighboring vehicles as task processing helpers, a greedy algorithm is proposed to solve the mentioned optimization problem. Besides, two bipartite matching based algorithms, named BMA1 and BMA2, are proposed by exploiting Kuhn-Munkras approach and minimum-cost maximum-flow approach, respectively. Performance of the proposed model and the offloading algorithms are evaluated on the combined simulation platform by open street map, SUMO and NS-3. Simulation results show that, the proposed model outperforms four existing models in terms of average response time, when the five models have similar number of unsuccessful tasks. Moreover, the proposed BMA1 and BMA2 are superior to the existing greedy algorithm in terms of the average response time of tasks, and the proposed greedy algorithm significantly accelerates the generation of offloading decisions in comparison to BMA1, BMA2 and the existing greedy algorithm.

This article defines the QoS-guaranteed efficient cloudlet deployment problem in wireless metropolitan area network, which aims to minimize the average access delay of mobile users, i.e., the average delay when service requests are successfully sent and being served by cloudlets. Meanwhile, we try to optimize total deployment cost represented by the total number of deployed cloudlets. For the first target, both un-designated capacity and constrained capacity cases are studied, and we have designed efficient heuristic and clustering algorithms, respectively. We show our algorithms are more efficient than the existing algorithm. For the second target, we formulate an integer linear programming to minimize the number of used cloudlets with given average access delay requirement. A clustering algorithm is devised to guarantee the scalability. For a special case of the deployment cost optimization problem where all cloudlets’ computing capabilities have been given, i.e., designated capacity, an efficient heuristic algorithm is further proposed to minimize the number of cloudlets. We finally evaluate the performance of proposed algorithms through extensive experimental simulations. Simulation results demonstrate the proposed algorithms are more than \(46\%\) efficient than existing algorithms on the average cloudlet access delay. Compared with existing algorithms, our proposed clustering and heuristic algorithms can reduce the number of deployed cloudlets by about \(50\%\) averagely, owing to the calculation processes of shortest paths between APs and the sorting processes of user access delays.

This article defines the QoS-guaranteed efficient cloudlet deploy problem in wireless metropolitan area network, which aims to minimize the average access delay of mobile users i.e. the average delay when service requests are successfully sent and being served by cloudlets. Meanwhile, we try to optimize total deploy cost represented by the total number of deployed cloudlets. For the first target, both un-designated capacity and constrained capacity cases are studied, and we have designed efficient heuristic and clustering algorithms respectively. We show our algorithms are more efficient than the existing algorithm. For the second target, we formulate an integer linear programming to minimize the number of used cloudlets with given average access delay requirment. A clustering algorithm is devised to guarantee the scalability. For

Cloudlet deployment and resource allocation for mobile users (MUs) have been extensively studied in existing works for computation resource scarcity. However, most of them failed to jointly consider the two techniques together, and the selfishness of cloudlet and access point (AP) are ignored. Inspired by the group-buying mechanism, this paper proposes three-stage auction schemes by combining cloudlet placement and resource assignment, to improve the social welfare subject to the economic properties. We first divide all MUs into some small groups according to the associated APs. Then the MUs in same group can trade with cloudlets in a group-buying way through the APs. Finally, the MUs pay for the cloudlets if they are the winners in the auction scheme. We prove that our auction schemes can work in polynomial time. We also provide the proofs for economic properties in theory. For the purpose of performance comparison, we compare the proposed schemes with HAF, which is a centralized cloudlet placement scheme without auction. Numerical results confirm the correctness and efficiency of the proposed schemes.