CQUIC: Cross-Layer QUIC for Next Generation Mobile Networks

Requirements for Next Generation Mobile Networks (NGMN) include low latency, higher throughput, scalability, and energy efficiency. As 5G millimeter wave (mmWave) band is short-range, the handover is inevitable. Google proposed QUIC (Quick UDP Internet Connection), which aims to address these challenges. However, Google QUIC (GQUIC), follows “WiFi-First” policy causing frequent network switching, which can lead to a throughput reduction and fast battery degradation. In this paper, we propose Cross-layer QUIC (CQUIC) framework, that follows “WiFi-if-best” policy to enhance the throughput and resilience by using a Cross-Layer approach. CQUIC proposes a novel migration scheme in QUIC which adapts to the dynamic network characteristics. GQUIC protocol with low bandwidth and high round-trip-time fail to migrate for seamless User Experience. CQUIC algorithm predicts Cross-Layer Score (CLS) which incorporates predicted Signal-to-Interference Noise Ratio (SINR), QUIC Bandwidth, round-triptime (RTT) stats from QUIC Session and models the handover decision pro-actively. Compared with state-of-the-art methods such as GQUIC and HTTP (using TCP) this paper reveals the significant benefits of the proposed method. A series of experimental results obtained in live air network over Samsung Galaxy S10 devices show CQUIC outperforms the GQUIC by 20%, TCP by 36% and MPTCP (Backup) by 17% in terms of throughput. Furthermore, CQUIC compared with MPTCP, reduces the data consumption over mobile network and operates green by reducing the power consumption by 25%.

[1]  Ion Stoica,et al.  HTTP as the narrow waist of the future internet , 2010, Hotnets-IX.

[2]  Sung-Jea Ko,et al.  Selective Channel Scanning for Fast Handoff in Wireless LAN Using Neighbor Graph , 2004, PWC.

[3]  Xiaofeng Wang,et al.  SMig: Stream Migration Extension for HTTP/2 , 2016, CoNEXT.

[4]  Elsa M. Macías,et al.  RSSI Prediction in WiFi Considering Realistic Heterogeneous Restrictions , 2014, Netw. Protoc. Algorithms.

[5]  Kayhan Zrar Ghafoor,et al.  A smart handover prediction system based on curve fitting model for Fast Mobile IPv6 in wireless networks , 2012, Int. J. Commun. Syst..

[6]  Martin Thomson,et al.  QUIC: A UDP-Based Multiplexed and Secure Transport , 2020, RFC.

[7]  Navrati Saxena,et al.  D-TCP: Dynamic TCP congestion control algorithm for next generation mobile networks , 2018, 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[8]  Janusz Gozdecki,et al.  Wireless link prediction and triggering using modified Ornstein–Uhlenbeck jump diffusion process , 2014, Wirel. Networks.

[9]  Henning Schulzrinne,et al.  Reducing MAC layer handoff latency in IEEE 802.11 wireless LANs , 2004, MobiWac '04.

[10]  Abhishek Majumder,et al.  Classification of Seamless Handoff Process in Wifi Network Based on Radios , 2019 .

[11]  Madhan Raj Kanagarathinam,et al.  CLEH — Cross layer enhanced handover for IMS sessions , 2018, 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[12]  E.J. Rivera-Lara,et al.  Analysis of the Relationship between QoS and SNR for an 802.11g WLAN , 2008, 2008 International Conference on Communication Theory, Reliability, and Quality of Service.

[13]  Fan Yang,et al.  The QUIC Transport Protocol: Design and Internet-Scale Deployment , 2017, SIGCOMM.

[14]  Tanja Lange,et al.  MinimaLT: minimal-latency networking through better security , 2013, IACR Cryptol. ePrint Arch..

[15]  Martin Thomson,et al.  Hypertext Transfer Protocol Version 2 (HTTP/2) , 2015, RFC.