Adaptive Bitrate Video Transmission Over Cognitive Radio Networks Using Cross Layer Routing Approach

Ali, Amjad; Tariq, Sikandar; Iqbal, Muddesar; Feng, Li; Raza, Imran; Ṣiddiqi, Muḥammad Zubair

doi:10.1109/tccn.2020.2990673

Cited by 25 publications

(11 citation statements)

References 37 publications

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“…In terms of QoE, our work complements and builds upon existing literature by developing a method to detect buffer stalls by tracking the buffer health of live video streams in real-time. The QoE metrics obtained from our system can be further used to augment: routing optimization systems like [20], or an adaptive scheduling systems like [21]. Our design choices primarily aim at scalability and ease of deployment by identifying inexpensive traffic attributes (to compute), and building machine learning models that are "general" (work across providers) and "simple" (lower-memory footprint, and ease of training and deployment).…”

Section: Related Workmentioning

confidence: 99%

Modeling Live Video Streaming: Real-Time Classification, QoE Inference, and Field Evaluation

Madanapalli¹,

Mathai²,

Gharakheili³

et al. 2021

Preprint

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Social media, professional sports, and video games are driving rapid growth in live video streaming, on platforms such as Twitch and YouTube Live. Live streaming experience is very susceptible to short-time-scale network congestion since client playback buffers are often no more than a few seconds. Unfortunately, identifying such streams and measuring their QoE for network management is challenging, since content providers largely use the same delivery infrastructure for live and videoon-demand (VoD) streaming, and packet inspection techniques (including SNI/DNS query monitoring) cannot always distinguish between the two.In this paper, we design, build, and deploy ReCLive: a machine learning method for live video detection and QoE measurement based on network-level behavioral characteristics. Our contributions are four-fold: (1) We analyze about 23,000 video streams from Twitch and YouTube, and identify key features in their traffic profile that differentiate live and on-demand streaming. We release our traffic traces as open data to the public; (2) We develop an LSTM-based binary classifier model that distinguishes live from on-demand streams in real-time with over 95% accuracy across providers; (3) We develop a method that estimates QoE metrics of live streaming flows in terms of resolution and buffer stall events with overall accuracies of 93% and 90%, respectively; and (4) Finally, we prototype our solution, train it in the lab, and deploy it in a live ISP network serving more than 7,000 subscribers. Measurements from the field show that 99.8% of Twitch videos are streamed live, while this measure is only 2.3% for YouTube. Further, during peak hours as many as 15% of live video streams are played at low-definition resolution and about 7% of them experience a buffer stall. Our method provides ISPs with fine-grained visibility into live video streams, enabling them to measure and improve user experience.

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Section: Related Workmentioning

confidence: 99%

Modeling Live Video Streaming: Real-Time Classification, QoE Inference, and Field Evaluation

Madanapalli¹,

Mathai²,

Gharakheili³

et al. 2021

Preprint

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“…One challenge is the large data volume. Compared with traditional video streaming [17], the transmission bandwidth for a point cloud video application with a frame rate of 30 frames per second (i.e., the standard video frame rate) can be as high as 6 Gbps [18], leading to considerable pressure for transmission and storage. Efficient point cloud video encoding or compression has, therefore, become important for high-quality point cloud video applications.…”

Section: A Point Cloud Video Encoding and Processingmentioning

confidence: 99%

Fuzzy Logic-Based Adaptive Point Cloud Video Streaming

Liu

Chen

et al. 2020

IEEE Open J. Comput. Soc.

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Point cloud video provides 6 degrees of freedom (6DoF) viewing experiences to allow users to freely select the viewing angles of 3D scenes and is expected to be the next-generation video. This paper studies the point cloud video streaming and proposes a fuzzy logic-based point cloud video streaming scheme to solve the inherent technical issues. In particular, a point cloud video is first partitioned into smaller tiles, along with a low-quality base layer covering the entire video. Each tile is encoded into different quality levels, and both the compressed and uncompressed (i.e., decoded) versions of each tile are prepared for selection. Then, based on the user's viewing angle and predicted future network bandwidth condition, fuzzy logic empowered quality level selection, with properly defined novel fuzzification, fuzzy rules, and defuzzification, is conducted to maximize the received point cloud video quality under the communication resource, computational resource and quality requirements constraints. Extensive simulations based on real point cloud video sequences and network traces are conducted, and the results reveal the superiority of the proposed scheme over the baseline scheme. To the best of our knowledge, this is the first work studying point cloud video streaming using fuzzy logic.

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“…A cross-layer routing approach is proposed to improve the QoS parameters for multimedia applications in CR Networks. However, in this solution, the routing is performed in a centralized way, and hence, it is not applicable for the distributed environment such as CRAHN [5]. Several learning solutions for CRAHN have been proposed to address the load balancing and characterization of channel stability of routing problem [13,14].…”

Section: Complexitymentioning

confidence: 99%

“…In infrastructure-based CR networks, SAP manages the network operations just like a base station in the cellular networks. On the other hand, SUs in CRAHNs can communicate with each other in a peer to peer fashion [5].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Routing Protocol to Minimize Channel Switching and Interference for Cognitive Radio Networks

Malik

Hasan

2020

Complexity

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In the existing network-layered architectural stack of Cognitive Radio Ad Hoc Network (CRAHN), channel selection is performed at the Medium Access Control (MAC) layer. However, routing is done on the network layer. Due to this limitation, the Secondary/Unlicensed Users (SUs) need to access the channel information from the MAC layer whenever the channel switching event occurred during the data transmission. This issue delayed the channel selection process during the immediate routing decision for the channel switching event to continue the transmission. In this paper, a protocol is proposed to implement the channel selection decisions at the network layer during the routing process. The decision is based on past and expected future routing decisions of Primary Users (PUs). A learning agent operating in the cross-layer mode of the network-layered architectural stack is implemented in the spectrum mobility manager to pass the channel information to the network layer. This information is originated at the MAC layer. The channel selection is performed on the basis of reinforcement learning algorithms such as No-External Regret Learning, Q-Learning, and Learning Automata. This leads to minimizing the channel switching events and user interferences in the Reinforcement Learning- (RL-) based routing protocol. Simulations are conducted using Cognitive Radio Cognitive Network simulator based on Network Simulator (NS-2). The simulation results showed that the proposed routing protocol performed better than all the other comparative routing protocols in terms of number of channel switching events, average data rate, packet collision, packet loss, and end-to-end delay. The proposed routing protocol implies the improved Quality of Service (QoS) of the delay sensitive and real-time networks such as Cellular and Tele Vision (TV) networks.

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Adaptive Bitrate Video Transmission Over Cognitive Radio Networks Using Cross Layer Routing Approach

Cited by 25 publications

References 37 publications

Modeling Live Video Streaming: Real-Time Classification, QoE Inference, and Field Evaluation

Modeling Live Video Streaming: Real-Time Classification, QoE Inference, and Field Evaluation

Fuzzy Logic-Based Adaptive Point Cloud Video Streaming

Reinforcement Learning-Based Routing Protocol to Minimize Channel Switching and Interference for Cognitive Radio Networks

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